{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20d1844d-56a1-4d97-9098-04cd38614402",
   "metadata": {},
   "source": [
    "# Using time to model a linear trend"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7e22b9-366f-4754-ba9c-c642f5b6f291",
   "metadata": {},
   "source": [
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In this notebook, we show how to create a feature using time to capture the trend of a time series. We will use it to create a simple forecasts with a linear regression. We will also show how tree based models are unable to extrapolate into the future when there is trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a86e7d9-212c-4d82-b896-20a4e078c336",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51d42e9-8edd-416d-8ac1-9a77792a9ed8",
   "metadata": {},
   "source": [
    "# Data set synopsis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e964ccb-f3fd-4e88-a08a-b82e6fc56074",
   "metadata": {},
   "source": [
    "The air passengers dataset is the monthly totals of international airline passengers, from 1949 to 1960, in units of 1000s. \n",
    "\n",
    "For instructions on how to download, prepare, and store the dataset, refer to notebook number 5, in the folder \"01-Create-Datasets\" from this repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b41fdf4-7f04-4fa6-ae61-5831d3b36473",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    \"../Datasets/example_air_passengers.csv\",\n",
    "    parse_dates=[\"ds\"],\n",
    "    index_col=[\"ds\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3cc441-df77-439b-a88d-e74c334635c3",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e096e012-faba-477d-b0b3-6d2a2aac6407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "data.plot(y=\"y\", marker=\".\", figsize=[10, 5], legend=None, ax=ax)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Air passengers (1000s)\")\n",
    "ax.set_title(\"Air passenger numbers\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67971d8a-7f20-4a42-b109-11b07d4605ba",
   "metadata": {},
   "source": [
    "# Creating the time feature to capture trend (i.e., time since the start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "377f42e5-0559-4b56-b36a-1fa7387acfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22494627-6289-46d2-b404-23620f423f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y\n",
       "ds             \n",
       "1949-01-01  112\n",
       "1949-02-01  118\n",
       "1949-03-01  132\n",
       "1949-04-01  129\n",
       "1949-05-01  121"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "630516e8-bc4f-48b2-804e-420dc4b0e34c",
   "metadata": {},
   "source": [
    "Let's calculate the number of months since the start of the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91711c6a-a6d5-4191-bf9b-43766c3cd1e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y    t\n",
       "ds                  \n",
       "1949-01-01  112  0.0\n",
       "1949-02-01  118  1.0\n",
       "1949-03-01  132  2.0\n",
       "1949-04-01  129  3.0\n",
       "1949-05-01  121  4.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"t\"] = np.round((df.index - df.index.min()) / np.timedelta64(1, \"M\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b6d4d4-6ae5-4275-821f-1b8fcfb90966",
   "metadata": {},
   "source": [
    "# TimeSince transformer from sktime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9b761ca-3caf-49ce-b973-975c265043e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Requires version 0.14.1 or greater of sktime.\n",
    "from sktime.transformations.series.time_since import TimeSince"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd575c4b-dcc9-4d86-b33b-0f1d64b06027",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start with the original data again.\n",
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b664575a-50ba-4fa9-baa1-c1e227446acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = TimeSince(\n",
    "    start=[\"1949-01-01\", \"1949-02-01\"],  # A list of start dates.\n",
    "    # If `None`, uses earliest time in dataframe.\n",
    "    to_numeric=True,  # Convert output to integer or keep as time-like.\n",
    "    # Default is `True`\n",
    "    freq=\"MS\",  # Specify time series frequency if not specified in dataframe.\n",
    "    # Default is `None` and it is inferred from the dataframe index.\n",
    "    positive_only=False,  # Set negative values to zero.\n",
    "    # Default is `False`\n",
    "    keep_original_columns=False,  # Keep the other columns in the dataframe\n",
    "    # after passing to `.transform()`.\n",
    "    # Default is `False`\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f1bb51b-c72a-4706-82b7-bce402c64709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TimeSince(freq=&#x27;MS&#x27;, start=[&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TimeSince</label><div class=\"sk-toggleable__content\"><pre>TimeSince(freq=&#x27;MS&#x27;, start=[&#x27;1949-01-01&#x27;, &#x27;1949-02-01&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TimeSince(freq='MS', start=['1949-01-01', '1949-02-01'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer.fit(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c91caf-e180-4216-ba51-c0e027c95891",
   "metadata": {},
   "source": [
    "Let's look at some of the attributes after fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f95c2043-e212-4a82-965c-4435a26da98e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['1949-01-01', '1949-02-01'], 'MS']\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    [\n",
    "        transformer.start,\n",
    "        transformer.freq,\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cd226f-6b0e-4335-8d99-d4df7328e283",
   "metadata": {},
   "source": [
    "Because we specified `start` there are no learned parameters from the data itself. When we use `.transform()` the transformer will compute for each date in `start`: `df.index - start_date` (i.e., the time since the specified start date) and convert this to an integer output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c28f9027-be29-4782-aa61-6a1496b85fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_since_1949-01-01 00:00:00</th>\n",
       "      <th>time_since_1949-02-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>139</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>140</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>141</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>142</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>143</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            time_since_1949-01-01 00:00:00  time_since_1949-02-01 00:00:00\n",
       "ds                                                                        \n",
       "1949-01-01                               0                              -1\n",
       "1949-02-01                               1                               0\n",
       "1949-03-01                               2                               1\n",
       "1949-04-01                               3                               2\n",
       "1949-05-01                               4                               3\n",
       "...                                    ...                             ...\n",
       "1960-08-01                             139                             138\n",
       "1960-09-01                             140                             139\n",
       "1960-10-01                             141                             140\n",
       "1960-11-01                             142                             141\n",
       "1960-12-01                             143                             142\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer.transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226f126f-ee73-4313-9030-66868fa30fec",
   "metadata": {},
   "source": [
    "If we do not specify the `start` dates then the transformer will automatically use the earliest date in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0848a30d-42ec-4a84-95e3-b4abc0444618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            time_since_1949-01-01 00:00:00\n",
       "ds                                        \n",
       "1949-01-01                               0\n",
       "1949-02-01                               1\n",
       "1949-03-01                               2\n",
       "1949-04-01                               3\n",
       "1949-05-01                               4\n",
       "...                                    ...\n",
       "1960-08-01                             139\n",
       "1960-09-01                             140\n",
       "1960-10-01                             141\n",
       "1960-11-01                             142\n",
       "1960-12-01                             143\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = TimeSince()\n",
    "transformer.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65f9098-ccb5-4c55-a512-19d87b622bdc",
   "metadata": {},
   "source": [
    "To keep the original columns we can set the `keep_original_columns` flag to `True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4af8493c-273b-40cd-bf11-410db97ac09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>time_since_1949-01-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              y  time_since_1949-01-01 00:00:00\n",
       "ds                                             \n",
       "1949-01-01  112                               0\n",
       "1949-02-01  118                               1\n",
       "1949-03-01  132                               2\n",
       "1949-04-01  129                               3\n",
       "1949-05-01  121                               4\n",
       "...         ...                             ...\n",
       "1960-08-01  606                             139\n",
       "1960-09-01  508                             140\n",
       "1960-10-01  461                             141\n",
       "1960-11-01  390                             142\n",
       "1960-12-01  432                             143\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = TimeSince(keep_original_columns=True)\n",
    "transformer.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d20fba2-1d7b-472c-a73a-d67e55173bb7",
   "metadata": {},
   "source": [
    "# Let's build a forecast with just the time feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88b07c6a-983a-4205-83ad-2cf1f841c02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4192c439-97ce-49d9-bfa0-e0749229c8e1",
   "metadata": {},
   "source": [
    "In this example, we only have 1 exogenous feature (the time) and no features are built from the target variable. This makes the forecasting workflow simple in this case (no need to worry about direct or recursive forecasting), we'll show more complex examples later this section! Here, we just want to focus on how the single time feature impacts a forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83338a11-0935-47b5-a62f-297dda8823dc",
   "metadata": {},
   "source": [
    "![](images/forecast_with_just_time.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "130d5c18-2870-4a1a-89af-c09bdff6b775",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
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       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              y\n",
       "ds             \n",
       "1949-01-01  112\n",
       "1949-02-01  118\n",
       "1949-03-01  132\n",
       "1949-04-01  129\n",
       "1949-05-01  121"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new copy of the original data\n",
    "df = data.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1495eb-8b91-4452-8993-fd03c47cc634",
   "metadata": {},
   "source": [
    "Note: In this example, where we are just creating a feature derived from the time index, it does not matter whether we create the time feature before or after splitting the data into a train and test set. This is because we cannot create look ahead bias with this feature, we are just using the time index to create a feature where we know its value in the past and the future. We would arrive at exactly the same dataframes being passed to the model during fitting and predicting irrespective of whether we create this feature first or split the data first.\n",
    "\n",
    "Nevertheless, when building a feature engineering pipeline with other transformers which learn parameters from the training data we will want to split our data first, so we shall continue with this workflow.\n",
    "\n",
    "Let's reserve the last 24 data points as hold out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d8d464c-b6ad-4c05-98f8-ca3ead54199b",
   "metadata": {},
   "outputs": [],
   "source": [
    "holdout_size = 24\n",
    "df_train = df.iloc[:-holdout_size]\n",
    "df_test = df.iloc[-holdout_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a397abf0-8919-4e60-a7cb-693eec2514ec",
   "metadata": {},
   "source": [
    "Create the time feature (i.e., the time since the start of the time series). We'll let the transformer infer the `start` parameter from our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40cabbca-fad6-4ded-a5dc-6f68132817b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the transformer\n",
    "transformer = TimeSince(keep_original_columns=True, freq=\"MS\")\n",
    "transformer.fit(df_train)\n",
    "\n",
    "# Create the time feature\n",
    "df_train = transformer.transform(df_train)\n",
    "df_test = transformer.transform(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "840960ba-4b01-4ef7-8e04-41999ff7e60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>time_since_1949-01-01 00:00:00</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y  time_since_1949-01-01 00:00:00\n",
       "ds                                             \n",
       "1949-01-01  112                               0\n",
       "1949-02-01  118                               1\n",
       "1949-03-01  132                               2\n",
       "1949-04-01  129                               3\n",
       "1949-05-01  121                               4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>time_since_1949-01-01 00:00:00</th>\n",
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       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>505</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>404</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>359</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>310</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>337</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y  time_since_1949-01-01 00:00:00\n",
       "ds                                             \n",
       "1958-08-01  505                             115\n",
       "1958-09-01  404                             116\n",
       "1958-10-01  359                             117\n",
       "1958-11-01  310                             118\n",
       "1958-12-01  337                             119"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>time_since_1949-01-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>360</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>342</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>406</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>396</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>420</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y  time_since_1949-01-01 00:00:00\n",
       "ds                                             \n",
       "1959-01-01  360                             120\n",
       "1959-02-01  342                             121\n",
       "1959-03-01  406                             122\n",
       "1959-04-01  396                             123\n",
       "1959-05-01  420                             124"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sanity check\n",
    "display(df_train.head())\n",
    "display(df_train.tail())\n",
    "display(df_test.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddcae064-393d-40df-9e2c-03e43d0f4f28",
   "metadata": {},
   "source": [
    "Create X and y for training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07cb9105-67ef-4b42-bbb0-e80ed11ac79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"time_since_1949-01-01 00:00:00\"]\n",
    "target = [\"y\"]\n",
    "\n",
    "y_train = df_train[target]\n",
    "X_train = df_train[features]\n",
    "\n",
    "y_test = df_test[target]\n",
    "X_test = df_test[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ada257-e1d7-498d-9d1e-5d8e471327d6",
   "metadata": {},
   "source": [
    "Create predictions for both the training and testing period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "102e6567-f29c-407d-9a12-5aadc35fdfc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make forecast and convert to dataframes.\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_train = pd.DataFrame(y_pred_train, index=X_train.index)\n",
    "\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_test = pd.DataFrame(y_pred_test, index=X_test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d90c28-932f-4dd1-b2bf-7d005102d208",
   "metadata": {},
   "source": [
    "Plot predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a495fc59-c545-4c97-8232-c12f20c41b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "\n",
    "df_train[target].plot(ax=ax, marker=\".\", label=\"train\")\n",
    "df_test[target].plot(ax=ax, marker=\".\", color=\"r\", label=\"test\", alpha=0.4)\n",
    "\n",
    "y_pred_train.plot(color=\"k\", ax=ax)\n",
    "y_pred_test.plot(color=\"k\", ax=ax, linestyle=\"--\")\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"y_pred train set\", \"y_pred test set\"])\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Air passengers\")\n",
    "ax.set_title(f\"Air passengers - {model}\")\n",
    "ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8189ff-bc60-4651-86a6-0edcf2bc1354",
   "metadata": {},
   "source": [
    "Let's see how this tree based models handle this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3ad3075-02af-4036-9156-8a677304f270",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeRegressor(max_depth=10)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make forecast and convert to dataframes.\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_train = pd.DataFrame(y_pred_train, index=X_train.index)\n",
    "\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_test = pd.DataFrame(y_pred_test, index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c27e09e6-44fb-406b-a29c-a8a2c67a37d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "\n",
    "df_train[target].plot(ax=ax, marker=\".\", label=\"train\")\n",
    "df_test[target].plot(ax=ax, marker=\".\", color=\"r\", label=\"test\", alpha=0.4)\n",
    "\n",
    "y_pred_train.plot(color=\"k\", ax=ax)\n",
    "y_pred_test.plot(color=\"k\", ax=ax, linestyle=\"--\")\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"y_pred train set\", \"y_pred test set\"])\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Air passengers\")\n",
    "ax.set_title(f\"Air passengers - {model}\")\n",
    "ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8815dea0-8a18-43b2-87de-b77db712809d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kishan_manani/.pyenv/versions/3.8.7/envs/udemy-ts/lib/python3.8/site-packages/sklearn/utils/validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "model = HistGradientBoostingRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make forecast and convert to dataframes.\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_train = pd.DataFrame(y_pred_train, index=X_train.index)\n",
    "\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_test = pd.DataFrame(y_pred_test, index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21265d0b-a42e-4a76-b1a5-87b31307abc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "\n",
    "df_train[target].plot(ax=ax, marker=\".\", label=\"train\")\n",
    "df_test[target].plot(ax=ax, marker=\".\", color=\"r\", label=\"test\", alpha=0.4)\n",
    "\n",
    "y_pred_train.plot(color=\"k\", ax=ax)\n",
    "y_pred_test.plot(color=\"k\", ax=ax, linestyle=\"--\")\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"y_pred train set\", \"y_pred test set\"])\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Air passengers\")\n",
    "ax.set_title(f\"Air passengers - {model}\")\n",
    "ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c3cf75-e7ca-4105-bb74-fa3c77c086f1",
   "metadata": {},
   "source": [
    "# Let's build a forward looking forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7250db93-ca95-4f5b-a45d-48f1d0f91af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y\n",
       "ds             \n",
       "1949-01-01  112\n",
       "1949-02-01  118\n",
       "1949-03-01  132\n",
       "1949-04-01  129\n",
       "1949-05-01  121"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new copy of the original data\n",
    "df = data.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "14bb9691-bca3-487b-b5f3-dd83b27cb77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-08-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-09-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-10-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-11-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-12-01</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [1961-01-01 00:00:00, 1961-02-01 00:00:00, 1961-03-01 00:00:00, 1961-04-01 00:00:00, 1961-05-01 00:00:00, 1961-06-01 00:00:00, 1961-07-01 00:00:00, 1961-08-01 00:00:00, 1961-09-01 00:00:00, 1961-10-01 00:00:00, 1961-11-01 00:00:00, 1961-12-01 00:00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast_start_point = df.index[-1] + pd.DateOffset(months=1)\n",
    "\n",
    "# forecasting horizon\n",
    "fh = pd.date_range(\n",
    "    start=forecast_start_point,  # first period after the\n",
    "                                 # end of our time series\n",
    "    freq=\"MS\",\n",
    "    periods=12,  # forecast 12 periods into the future\n",
    ")\n",
    "\n",
    "# Create our features for our training and prediction periods\n",
    "\n",
    "df_train = df  # Train over the whole dataset\n",
    "df_predict = pd.DataFrame(index=fh)  # Predict over our forecast horizon\n",
    "\n",
    "display(df_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e9c813-79b7-4915-81ea-8ec752ec6f19",
   "metadata": {},
   "source": [
    "Let's specify the start date manually just to show how it works.\n",
    "\n",
    "We can actually pick any start date and we'll get *exactly* the\n",
    "same results with linear and tree based models. This is because\n",
    "this feature is just a measure of time from some reference time.\n",
    "The choice of reference doesn't actually matter because\n",
    "the time difference between any two time points is the same \n",
    "irrespective of our choice of reference time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f1cb8f5a-dc42-431e-a1c0-c5b01da73812",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = TimeSince(\n",
    "    start=[pd.to_datetime(\"1970-02-01\")],  # Pick a start date\n",
    "                                           # far in future to\n",
    "                                           # show it has no impact.\n",
    "    freq=\"MS\",\n",
    "    keep_original_columns=True,\n",
    ")\n",
    "\n",
    "df_train = transformer.fit_transform(df_train)\n",
    "df_predict = transformer.transform(df_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5fd50107-f857-439a-921b-f8e24067f518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>time_since_1970-02-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "      <td>-253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "      <td>-252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "      <td>-251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "      <td>-250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "      <td>-249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              y  time_since_1970-02-01 00:00:00\n",
       "ds                                             \n",
       "1949-01-01  112                            -253\n",
       "1949-02-01  118                            -252\n",
       "1949-03-01  132                            -251\n",
       "1949-04-01  129                            -250\n",
       "1949-05-01  121                            -249"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_since_1970-02-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>-109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-01</th>\n",
       "      <td>-108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-01</th>\n",
       "      <td>-107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-01</th>\n",
       "      <td>-106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-01</th>\n",
       "      <td>-105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            time_since_1970-02-01 00:00:00\n",
       "1961-01-01                            -109\n",
       "1961-02-01                            -108\n",
       "1961-03-01                            -107\n",
       "1961-04-01                            -106\n",
       "1961-05-01                            -105"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_train.head(), df_predict.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e13ad536-4984-4be9-a9df-9061cc94342a",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"time_since_1970-02-01 00:00:00\"]\n",
    "target = [\"y\"]\n",
    "\n",
    "y_train = df_train[target]\n",
    "X_train = df_train[features]\n",
    "\n",
    "X_test = df_predict[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e57cc41b-b346-4dbc-b7ed-617de3f3286b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make forecast and convert to dataframes.\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_train = pd.DataFrame(y_pred_train, index=X_train.index)\n",
    "\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_test = pd.DataFrame(y_pred_test, index=X_test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5d4973-2b1c-43ed-bd6b-27c0ab81d57f",
   "metadata": {},
   "source": [
    "Plot predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "50d07fb4-12c6-44bc-9fdc-863e459b7acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d1pkjGF7i4RgTUW/AeKKwHyPz/bBD0B3EeErwPQwby8SuxtNOrEeUUu9heMCnaa3XY5Rz2wB87vCI78D4+zAIwyf9Fsb3EIynIHOAZUqpP2EI1WtpSqi0NW/FPY4CpdT3MbK9+x2Z7eM0Vdy4HiN7mwf8XCk1G+P6z8W4ufgqxnv7vGOXwzCehi7BeDpRhvF06PuObda3EctBh+3nYcd78D5NZd5igNs6Ob9BEM5dAlE6o6d/aKXMG0bJn3ogvIP7+RrGH9EPAVOgz0t+zu0fx3WkgdHtjDuIYb+IdLzOw3uZt3U+Hv8Nx/FTMB7DlmB4JdcAE72Mvx7DJlGDIVLfwxCIHsfGEEOfYAiHeows12fAjW5jMjF8oPsxhGaN4/9P4VZSzG38tzD+wFdiiNE8jGoEN7uNGeg4n4Vetl/oWDew2fKbMDJs9Rjl2n5FU2m5bzQbG4JRbutLR7w1GI/J/wXMdhs3g2Yl09zWjcPwuR5xbF+JUQnkpx39PeTjZ+yMpbWfA63F3M55vIGjQEiz5aMwvLwnMW6iCoGNwC+ARLdxAzCu/7OOa26r431v8Tm1dqwOnrfX8nYYwtEGrHVbloxRK/iQ4xorxxDsvwdGePkcVzliL8UQ0M6bqlfdxrV6TbqNuQLjBrTI8Z6dwqi68lMgwu183se47uscx9yCcUPpLHOYhOH13+mIvc5xnb0MpDU7pkeZN7fl38W4ObA4rs2VwDQv41rb/k66UFJVfuQnmH6cX6zzGqXU9Ri/gK7SWq9zW34VhhhYqLV+stk24Rh/sJwF3a/EeIy4CcOHWd8z0QtC96CMTlrf1lrLhBo3lFI/xSgpdpnWurk3WxC8opSaiGHdeUzL00VBOOc5ry0WSqmfO/7rLKfzLWW0zSzXWv9Ja71WKfUPjJnAEzHKTNkxHlV9A6Nb1yql1ACaPFofYZTecj/URm1MNhIE4RzBUYnGpt0eHTs8yD/EyKZnByo2IbhRSkVqNzuMw8PvbL+9MjBRCYLgT85rgYwxGcMdZ9OF4xhVKMDwWG13/PssxqPWXIz2sDsdYzJpqj/6ipfj3IXM8BWEc41BGKW/3sP4zqdhtDvOBL6v3RomCEIzdiql1mBYMKIx/MDTMOpZS9lPQTgPuCAsFoIgtORCt1goo3vbnzA8oKkY8wtygJe01h8EMjYhuFFKPY8hijMwEk25GF7057TW1kDGJgiCfzivBLJSqhFj5nHzDkyCIAiCIAiC4E4cYNdat3BUnG8C2Q6o+Pjm3VgFQRAEQRAEoYmKigowquS06AtyvnmQK+Pj4+PLy8sDHYcgCIIgCIIQxCQkJFBRUeHVdXChd9ITBEEQBEEQBA9EIAuCIAiCIAiCGyKQBUEQBEEQBMENEciCIAiCIAiC4IYIZEEQBEEQBEFwQwSyIAiCIAiCILhxvpV56xA1NTVUVlbS2NiI3W4PdDiC0C2YTCYiIiJITk5GqQuyWZ4gCIIgdIoLSiDb7XZOnTpFVVUVJpOJ0NBQzGZzoMMShG7BarVSXV1NfX09/fr1E5EsCIJwgVFUZSG/tI6MxEhSYyMCHc45xQUlkCsqKqiqqiI5OZmkpCRMJnGYCOc3paWlFBYWUlxcTEpKSqDDEQRBEHqIxdkFPL4kh1CTCavdzjM3jGbBhPRAh3XOcEEpxOrqasLCwkhOThZxLFwQJCYmEh4ejsViCXQogiAIQg9RVGXh8SU5WKx2quobsVjtPL4kh6Iq+VvQUS4olWi32wkJCZFHzcIFhdlsFq+9IAjCBUR+aR2mZlon1GQiv7QuQBGde1xQAlkQBEEQBOF8JyMxEqvNMzFitdnJSIwMUETnHiKQBUEQBEEQziNSYyO4bmxfj2VfGZsmE/V8QASyIAiCIAjCeUafeE8xvHp/ERW11gBFc+4hAlnoEEopFi5cGOgwBEEQBEHoAKcrjAl5VwxJIjrMTFmtlZdXHwpwVOcOARfISqlLlFL/U0qVKaWqlVK7lFJ3Bjquc5HNmzezcOFCysvLAx2KIAiCIAgBpLDSEMij+sXz46uHAvDmxjz+s+OkVLPoAAEVyEqpecAXQCjwC+CnwCogI5Bxnats3ryZJ598slsEcl1dHT//+c/9vl9BEARBEPzPGUcGOS0ugruuGEhSdCh2DQ9+uIsrn1vL4uyCAEcY3ASsUYhSKh54A/iz1vongYrjQsRms9HY2Eh4eHiHt4mIEGO/IAiCIJwrFFbWA4YXuaLOSqWlEQCbXWOzax5fksPUockyca8VAplBvhVIAH4JoJSKVed4geKiKgvbj5cF5NHFwoULeeCBBwDIzMxEKYVSiry8PJRS3H///bz11lsMHz6c8PBwNm3aBMALL7zA5ZdfTlJSEpGRkUycOJGPPvqoxf6be5AXLlyIUorc3FzuuOMO4uPjiY+P56677qK2trZHzlkQBEEQhJZUWaxU1xuCuHdcBPmldUSEmD3GSF3ktglkq+lrgAPAfKXU80A6UK6U+ivwhNba1nwDpVR5O/uM70wgjTa7y8zeWZbtOcMLKw4SYlI02jUPzb6IuaP6dGpfafERhJh9u3dZsGABR48e5Z133uGll14iOTkZwNVeeMWKFbz//vv88Ic/JCEhgbS0NAB+//vfc91113HbbbfR0NDAe++9x0033cSnn37Ktdde2+5xb7zxRgYPHsyzzz5LdnY2//jHP0hNTeW5557z8awFQRAEQfAHTv8xGBlks0lhbdYwymqXushtEUiBPATDa/wG8DywA/gK8AgQAdzfU4GcrrAw7fm1ftlXvePf3yzdz2+W7u/UPtY/fBUZiVE+bTNmzBgmTpzIO++8w/XXX8/AgQM91h86dIh9+/YxbNiwFssjI5u+ID/60Y+YMGECL774YocE8iWXXMJf//pX1+uSkhJee+01EciCIAiCECDOVBhqxKQgJSacELOJZ24YzUMf7sKuwWxSPHPDaLFXtEEgLRYxQC/gl1rrX2itF2utvwN8CPxAKZXcfAOtdUJbP0BFz57CucPMmTNbiGPAQxyXlZVRUVHBtGnTyM7O7tB+77vvPo/X06ZNo6SkhMrKyq4FLAiCIAhCpzjjyCCnxIa7nkgvmJDuerI9e0RvFkxID1h85wKBzCA7jS//brb8X8BNwGRgaU8EkhYfwfqHr+r09iXV9dz8t83UNzY9vggPMfH+vZeSFNPxiXDu8fibzMxMr8s//fRTnn76aXbu3El9fb1reUft4P379/d43atXL8AQ23FxcZ2MVhAEQRCEznKmwpBYfeI89UT/xGgAqhwT9oTWCaRAPg2MBAqbLXe+7tVTgYSYTT5bGtzJSIzitwtG8/iSHEJNJqx2O8/cMJpx/XvsFNrFPVPsZP369Vx33XVceeWVvPrqq6SlpREaGsrrr7/Ou+++26H9ms1mr8u11l2KVxAEQRCEzuHMIDfvppcSayTtzlbVt9hG8CSQAnk7xkS9fsAxt+XOnP/ZHo+oCyyYkM7Uocnkl9aRkRgZEF+Pr0VAFi1aREREBMuXL/co+fb666/7OzRBEARBEHoIpwe5eQbZJZCrRSC3RyA9yB86/r3bucBR5u0eoAbYHIigukJqbAQTB/QKmOk9Otp4dNLRRiFmsxmlFDZbU8GQvLw8Pv74426IThAEQRCEnuBMpWGx6N08g+ywfZbWNGC12VtsJzQRMIGstd4OvAU8ppT6u1Lq+8B/gTnAk1prmeXlIxMnTgTgiSee4O233+a9996jpqam1fHXXnsttbW1zJ07l7/85S889dRTTJkyhSFDhvRUyIIgCIIg+BlnBrn5nCZnBhmgWLLIbRJIiwXAd4ETwLcdP8eA+7TWf21zK8Er48eP55lnnuGVV15h2bJl2O12cnNzWx0/c+ZMXnvtNZ599lnuv/9+MjMzee6558jLy2P37t09GLkgCIIgCP7AarNTUmOI397NLBapcU0C+WxVPWnxUge5NdT5NJlKKVUeHx8f35rF4Pjx4wAMGDCgB6MShMAi170gCMKFw8nyOq54dg0Aa346nUEpMa51WmuG/2IZ9Y12Xvv2JK6+uHegwgwKEhISqKioqHCUCvYgkB5kQRAEQRAEwY84S7xByyoWSimpZNFBRCALgiAIgiCcJzj9x3ERIUSFtXTSikDuGCKQBUEQBEEQzhNaq4HsJNUhkItEILeJCGRBEARBEITzhEKHQG4+Qc+JZJA7hghkQRAEQRCE84TTFYZAbl7izUlKjLFcmoW0jQhkQRAEQRCE84RCh0Bu3kXPiWSQO4YIZEEQBEEQhPMEpwe5eRc9J+4C+Xwq9etvRCALgiAIgiC0QVGVhe3HyyiqsgQ6lDbRWrsEcmsWC+ckvTqrjer6xh6L7Vwj0J30BEEQBEEQgpbF2QU8viSHUJMJq93OMzeMZsGE9ECH5ZWyWisNjXag/Ul6YGSRYyNCeyS2cw3JIAuCIAiCIHihqMrCI4t2Y7HaqapvxGK18/iSnKDNJJ+paIqrNQ9yUkyY6//iQ24dEciCIAiCIAheWLbnDFabp0831GQiv7SulS0Ci7PEW5jZRGJ0mNcx4SFmEqKMrLFUsmgdEciCIAiCIAjNyCuu4YUVB1sst9rtZCRGBiCi9nGWeOsdH45SqtVxrmYhlSKQW0ME8nnE5s2bWbhwIeXl5d2y/zNnzrBw4UJ27tzZLfsXBEEQhEBTVGXh84NnueP1LVTWeU5iiwg18cwNo0mN9W5fCDSuLnqt2CucuCpZSAa5VUQgn0ds3ryZJ598slsF8pNPPikCWRAEQTgvWZxdwJXPreWuN7/kREkdZgUPz73ItX7Vg9ODdoIeNNVAbm2CnpOUGKmF3B5SxUIQBEEQhAueoioLjy/JweKoAgFgMimmZCYGMCrfaK/EmxNpFtI+kkE+T1i4cCEPPPAAAJmZmSilUEqRl5cHwOuvv86ECROIjIwkOTmZb3/72xQWFnrsY9u2bcyZM4fk5GQiIyPJzMzkO9/5DgDr1q1j/PjxANx1112u/b/xxhs9do6CIAiC0F3kl9YRavKURREhZuqsTYI52AXlmY5mkEUgt4tkkM8TFixYwNGjR3nnnXd46aWXSE5OBiAlJYUnn3ySp556im9+85vce++9nD59mt///vd8+eWXbN++ncjISIqKipg9ezaZmZn8/Oc/Jzo6mtzcXJYsWQLAxRdfzG9+8xueeOIJ7r33XqZNmwbA5ZdfHrBzFgRBEAR/kZEYidVm91hmtdsZ1juGqDAztQ02ioJcULo8yO1kkJ0e6mA/n0AiAhlobGykoKAg0GG4SE9PJyTEt49mzJgxTJw4kXfeeYfrr7+egQMHApCXl8evf/1rnn/+eX7605+6xs+bN4/LL7+cN998k/vuu4+NGzdSVlbGwYMHSUlJcY175plnAOjduzfz58/niSee4LLLLuP222/v+okKgiAIQpCQGhvBE9dezC/+sxeAsJCmCXmpseHkldQGtaC0WG1U1FmBjlssSmvqsdk1ZlPrFS8uVEQgAwUFBWRmZgY6DBe5ubkugdtVlixZgtaaBQsWUFxc7Fo+ZMgQ0tLSWLduHffddx8JCQmu8ffccw8mk7hvBEEQhAuL8f17uf6/9P+mMiQ1FjDEc15JbVBbEtybhIS0I3idAtmuoaSmPmircgQSUUHnOYcPH8ZutzNo0CBSUlI8fk6dOsXZs2cBmD59OjfeeCPf+973SElJ4aabbuLtt9+moaEhwGcgCIIgCD1DQVktANFhZganxLiWN3l2g7ODHsAHX+a7/n/zXzezOLv1J+POKhYgPuTWkAwyhqUhNzc30GG4SE/3XwkZu92O2WwmKyvLa9HwXr2Mu2WlFB999BFbtmzhv//9L8uXL+eOO+7ghRde4IsvviAmJqbFtoIgCIJwPlFQZnTI69cr0uNvZkqQN9YoqrLw9w3HXK8tjUZL7KlDk71mhxOiQgk1K6w2LQK5FUQgAyEhIX6zNAQSbwJ48ODB2Gw2hg4d2qFznDJlClOmTOHpp5/mgw8+4Oabb+b999/n7rvvbrMrjyAIgiCc65wsdwjkBM9OealxDoEcpGIyv7QOhQKa2mI7W2J7E8hKKVJiwjlVYQnacwo0YrE4j4iOjgbwaBRyww03YDKZeOqpp1qMt9vtlJaWAlBWVobWnv3mx40bB4DFYml1/4IgCIJwvnDSkUFO7xXlsTzYG2tkJEZis7eswNFWS2wp9dY2kkE+j5g4cSIATzzxBLfccguhoaF89atf5amnnuLnP/85R48e5atf/SrR0dEcPXqURYsW8cQTT3DPPffw5ptv8uqrr3L99dczePBgampq+Mc//kFcXBzz588HYODAgSQmJvKXv/yF2NhYoqOjmTJlSlBNcBQEQRCEzuJusXAn1VFXuLi6HrtdYwqyqg+psRH06xXJidI6wswmTCbabYktArltRCCfR4wfP55nnnmGV155hWXLlmG328nNzeWJJ55g6NChvPzyy/zqV7/CZDLRv39/rr/+embNmgUYk/S2bt3K+++/T2FhIfHx8UyePJm3337bJYBDQkJ46623eOSRR7jvvvtobGzk9ddfF4EsCIIgnBe0arFwiMlGu6a0toFkt0luwUKlpRGAH1w1mFun9G+3MoVLIFeLQPaGCOTzjMcee4zHHnusxfJvfOMbfOMb32h1u/Hjx/Puu++2u/9rr72Wa6+9tksxCoIgCEKwUV3f6Koj3CKDHNskiIsq64NOIJfVNFBea8R+9fDeHSrbluIYIxlk74gHWRAEQRCECx6n/xggvVkGuVdUmKu2cDBmXI8V17j+PzA5qo2RTYjFom1EIAuCIAiCcMFzstyogRwWYmqRITaZlGtZUWXw1ULOdQjk5JhwYiNCO7RNsE88DDQikAVBEARBuOBxTdBLiPQ6CS+YS73lOQTyoOToDm/jzCBX1zdS29DYLXGdy4hAFgRBEAThgudkmfcJek6COePqzCBn+iCQ3X3VxVXSNbc5IpAFQRAEQbjgKWilgoUTZwY5GAWy04M8sBMZZIBDRZV+j+lcRwSyIAiCIAgXPCdbqYHsxFn1oagquDzIWmuXxcKXDPLSnNOu/3//nWwWZxf4PbZzmYAJZKXUDKWUbuVneKDiEgRBEASh6xRVWdh+vCzoBGVrFLi66LWSQY4NTg9yYWU9dVYbAINSOiaQi6osPL4kx/XaatM8viTnnPmseoJgqIP8MrC92bJTAYhDEARBEAQ/sDi7gEcX5RBiVti15pkbRrNgQnqgw2oVi9VGsaN8W6se5CAti3asuBoApaB/YsdKvOWX1hFqMmGhqT11qMlEfmldh2ooXwgEg0D+TGv9caCDEARBEASh6xRVWXh00W4abJoGI7HJ40tymDo0OWjF16nyphrIrVksnBnk2gYb1fWNxIQHg4SCvGKjPF3f+EgiQs0d2iYjMRKr3e6xzGq3k5Ho/dzPR7TWba4PCg+yUipWKRUcV5ogCIIgCJ3mcGE1Vpun+HBmJ4MVZ4tps0nRJ867iE91Wx5MtZBzHRnkjtorAFJjI3jmhtGEmpvK2T39tVFBewPTHZw5c4b6+tafBgSDQH4bqATqlFIrlFKjWxuolCpv6weI76mgBUEQBEFoycc7TtI8Nxfs2UnnBL0+cRGEmL1Lo+SYMNf/g8lm4SzxNjCp4wIZYMGEdN797qWu15MGJvo1rmBAa82ePXt4/vnnueqqq/jDH/7gWpeWloa9WRbdHb9kbZVS4VprX6+WBuAjIAsoBsYADwEblFKXaK0P+SM2QRAEQRB6hs3HSvhwu2c1hFCz4pkbRgd1drKgnQoWAOEhZhKiQimvtQbVRL3O1EB2MrF/L6LDzNQ02Mg5WeFTmbhgpaqqitWrV7N06VKysrIoKGi6Hs1mM//3f//neh0REUFDg/ca0B3OICul5imlFjZb9gOlVCVQo5R6VynVsf6GgNZ6o9b6Jq31P7XWn2itnwamA1HAr1rZJqGtH6Cio8cXgpd169ahlGLdunUBjUMpxcKFCwMagyAIwrnC8ZIafvLvHQCMzUhgiOOR//emDw7qCXrQZLFIb2WCnpNgq2TRaLNzotTwIGf6YLFwYjIpRvY1Hr7vOXXuS6hnnnmGpKQkbrjhBv7+97+7xHH//v353ve+xwMPPOAxXqmWHROd+GKx+BngKr+mlLoY+D1GxYmVwM3AD33YXwu01ruAVcDVXdmPcGGwefNmFi5cSHl5eaBD6XGeffZZPv7440CHIQiCABhVK2b+v88odAjHOSN6k+6oqGBxztQLYtqrgewkNchqIZ8sr3P5vX1pM+3OqH4OgXzy3BHIVVVVfPzxxzz44IMeNokBAwZgtVoJCQnhqquu4vnnn2fPnj3k5eXxl7/8hWuvvbbDx/DFYnExsNTt9c1AHTBZa12plHoX+DZG2baukI8IZKEDbN68mSeffJI777yThIQEv++/rq6OkJDgnDv67LPPcv3113P99dcHOhRBEC5wiqosPLo4B5u9yXn8hzWHmXVxbwDOVgdHtrUtTrbTRc9JsJV6c3bQCzGpdmNvjVH94gDYc7ISrXWbWdVAobVm//79ZGVlsXTpUtavX4/VagXgW9/6FuPHjwdg3rx5LF68mKuvvpq4uLguHdOXv/69MLzCTq4B1mitnf0J1wHzuxSNwSDgrB/2I/QwWmssFguRkcE3EcNms9HY2Eh4eHj7gx1ERASvX04QBCFYyC+to7mkCjWZXJPdioNcIFttdk5XOJuEtF1HODXIBLKzg17/pKhWJxe2x2hHBrmizkpBWR0ZHayl3BPU1NTw0EMPkZWVxfHjxz3WhYSEcMUVV2CxNGXzExMTueGGG/xybF/ezWJgABhl2YBLgPVu60OBjhXgM/aR4mXZVOAqYLkPcV3wvP766yil2LFjR4t1TzzxBBEREZSVlXVoX2+88QZKKb744gu++93v0qtXLxISErjnnnuoqqryGDtw4ECuv/56srKymDBhAhEREbz//vsAlJaW8uMf/5j09HTCw8O56KKLPGaPOikoKOD6668nOjqa1NRUHnjggTbLrjhZuHChy0uUmZmJUgqlFHl5eYDhK7r//vt56623GD58OOHh4WzatAmAF154gcsvv5ykpCQiIyOZOHEiH330UYtjNPcgL1y4EKUUubm53HHHHcTHxxMfH89dd91FbW1tuzEfPnyYG2+8kT59+hAREUF6ejq33HILFRVNj7XsdjsvvPACF198MeHh4aSlpfHjH/+Y6upqj7gqKip48803Xed95513tnt8QRCE7iAjMZLGZmXdrHY7mY6qCsEiJlvjTIUFZ/K7PYuFM4NcVBkc5+ScoNdZewXAoJQYIh31kwNps9Bac+DAAfbv3+9aFhUVxSeffOISx3379uWee+5h0aJFlJSUsG7dOi677LJuiceXDPIm4D6l1F5gnmPbLLf1Q4DT3jZshfeVUrXARgzxPQq41/H/hT7sx284xVVrpKSkEB1tXIQ2m438/Pw2xzuFEIDVauXkyZNtju/bty9hYWFtjvHGjTfeyA9/+EPeffdd12MGMC62d999l/nz59OrVy+f9vn973+fpKQkfv3rX7N3717++te/cvr0af73v/95jNu3bx+333473//+97n33nsZPnw4NTU1TJ8+ncLCQu677z769evH2rVr+clPfkJZWRm/+pUxB7Ouro6rr76aEydO8JOf/IS0tDTefvtt1qxZ0258CxYs4OjRo7zzzju89NJLJCcnA8Zn5GTFihW8//77/PCHPyQhIYG0tDQAfv/733Pddddx22230dDQwHvvvcdNN93Ep59+2iF/0o033sjgwYN59tlnyc7O5h//+Aepqak899xzrW7T0NDAnDlzMJvNPPjggyQlJZGfn8+nn35KeXk58fHGHfzdd9/Nu+++y3e+8x3uv/9+Dh8+zJ/+9Cf27dvHqlWrUErx9ttv873vfY+JEydy7733AjB48OB24xYEQegOUmMj6NsrgvzSOsLMJkwmeOaG0W4ZZO9VAoKFk25NQtLi235y6KyFHCy2kc6WeHPHbFKM6BvH9uNl5JysYN7oNH+F1y41NTWsXbvWZZ3Iy8vj1ltv5V//+hdgJIQeeOABGhsbmTdvHmPGjOk5C4jWukM/wEigELA7fl53W6eAPPdlHdjf/wFbgBLACpwE/gn07+g+vOyzPD4+XrdGXl6ezsvLa3U90ObPRx995BpbXFzc7vi1a9e6xh88eLDd8bt37241tva45ZZbdHp6urbb7a5lGzZs0IBetGhRh/fz+uuva0BPnjxZW61W1/Inn3xSA3rdunWuZQMGDNCAXr16tcc+nnrqKR0bG6uPHTvmsfy+++7TERERurS0VGut9csvv6wBvXjxYteYmpoaPWTIkBbvnzdeeuklDejc3NwW6wBtNpv1wYMHW6yrra31eN3Q0KBHjRqlZ86c2WIfv/rVr1yvf/WrX2lA33vvvR7jbrjhBp2UlNRmrDt27NCA3rp1a6tjPv/88xbXmdZav/feexrQWVlZrmXx8fH629/+dpvHdNLedS8IgtAV7Ha7HvGLLD3gkU/1H1Yd0oWVdVprrTceKdYDHvlUD3jkU93QaAtwlK3z0bZ8PeCRT/Wkp1e2O9b9nOqtgT+nK55drQc88ql+Z3PXfsf/8uMcPeCRT/W3Xtvip8haJzc3V7/00kt69uzZOjw8vIUWGj16tIeW6U7i4+M1UK69aMoOWyy01nsxJup9DZihtb7LbXUC8BI+TNDTWv9Baz1Fa52ktQ7VWvfTWn9Ha32io/sQmrjjjjsoKCjgs88+cy3717/+RUJCgk+zNp1873vf85ig9sMfGgVKsrKyPMYNHTqUmTNneiz76KOPmD59OrGxsRQXF7t+Zs+ejcViYcuWLQAsXbqUjIwMj4lmUVFRrqxoV5k5cybDhg1rsdzdI11WVkZFRQXTpk0jOzu7Q/u97777PF5PmzaNkpISKisrW9kCV4b4v//9b6s1Fz/66CMSExOZPn26x/t25ZVXYjabA172ThAEwRunKyzUOCpVzBnVx1XpISW26YloSRBnkTs6QQ8gNa5pHkugvdUWq80Ve2dqILvjXslCt9OC2VfcPcIAixcv5oEHHmDFihXU19djNpuZOnUqv/nNb9ixYwe7du0KiomCHbJYKKWigZ8CW7TW/22+XmtdhlHy7ZwmNze3zfXuj+8TEhLaHd+nTx/X/zMzM9sd37dv3w5E6Z3Zs2fTu3dv3n33XWbMmIHVauWDDz7g61//uk8T05wMHTrU43VSUhK9evVqYUPJzMxsse3hw4fZvXu3x/vlztmzxhzM48ePM2TIkBZfhIsuusjneL3hLTaATz/9lKeffpqdO3d6+J07+oXs37+/x2unfaWsrKzVWbOZmZk8+OCD/PrXv+all15i+vTpfPWrX+XWW28lNjYWMN630tLSdt83QRCEYOJQoTE/JcSkPB71p8Q02RWKq+vp0459IVAUlBlzSNLb8R9D0yQ9MGoh9+1k5Qh/cKK0FqeW9ZdALq1p4HSFpcvndfjwYbKyssjKymLdunUcOXKEfv36ATB//nx+97vfMXfuXObNm8esWbN8toH2BB0SyFrrGqXU48CPujmegDJw4MAOjzWbzT6NDw0N9Wm8r5jNZm699VbeeOMN/vSnP7FixQpKSkq4/fbbu+2YgNeKFXa7nblz5/LTn/7U6zYjR47s1piceItt/fr1XHfddVx55ZW8+uqrpKWlERoayuuvv867777bof2azd7norZ31/3//t//46677uI///kPy5cv5wc/+AG/+c1v2LRpE/369cNut5OWlsZbb73ldfuu3EAJgiB0F0eKjEnEA5OjCQtpejAdFxlCmNlEg80e1BP1nD7eXtHt9zqLCQ8hItSExRr4c3LGHWY2YWpRR8Q3hqbGEB5ior7RTs7JCp8Fcl1dHevWrXOJ4iNHjnisX7ZsGXfffTdgJMFOnjyJydS5qhs9hS+T9I4CfdodJQSMb33rW7z00ktkZWXx3nvv0b9/f6688spO7evw4cNMmzbN9bqkpISysjIGDBjQ7raDBw+mrq6Oa665ps1xAwYMYP/+/S3qLh48eLBDMXbmEcyiRYuIiIhg+fLlHpn1119/3ed9dYZRo0YxatQonnjiCbZs2cKll17KX/7yF379618zePBg1q5dy7Rp09rN+gfD4ydBEARoyiAPTY3xWK6UIjkmjFMVlqCZ1NacxdkFbMszqjz9e0s+4zN6tdn1TylFamwEJ0prA94s5JNdpwBotNuZ/sJanrlhdKc7FoaYTQxPi2NXfjl7TlYwZ2TH5V5jYyMZGRmUlJR4LO/duzdz585l/vz5zJ4927XcWX0p2PFFvr8KfFcpldRdwQhdY/z48YwaNYq//e1vfPLJJ3zzm9/s9EX417/+lcbGRtfrV155BTCKcLfH17/+dT7//HOvntni4mJXpnX+/Pnk5+d7dISrra3lb3/7W4didFYU8aWTntlsRimFzdbU2SkvL6/bu9JVVlZ6vJ9giOWQkBCXP+vrX/86DQ0NPPvssy22r6+v9/A4R0dHX5AdBAXhfKeoysL242UBF1++cNiRQR7aO7bFuuQgqxvsTlGVhccX5+B89tdo1zy+JKfd9z41CEq95ZfWsnS3UTjMrsFitXco9rYY1dfZMMR7qTeLxcLy5cv5yU9+wrRp01wd7EJCQrj00ksxmUxcdtllPPXUU2zbto1Tp07xxhtv8I1vfKNbmnl1N75kkKuAUuCgUupN4DDQovir1tr782GhR/jWt77FI488AtAle0VdXR2zZs3ixhtvdJV5mzNnDjNmzGh324cffpj//Oc/zJkzh+985zuMGzeOyspKdu3axaJFi6iqqiIkJITvfve7/OlPf+K2227jJz/5CX369OHtt98mKqpjRconTpwIGLWeb7nlFkJDQ/nqV7/qEs7euPbaa3nxxReZO3cut956K0VFRbzyyisMGTKE3bt3d+i4nWHNmjX86Ec/4utf/zoXXXQRNpuNd955B6UUN954IwBXXXUV99xzDwsXLmT79u1cffXVmEwmDh06xAcffMC//vUvV1Z+4sSJrFq1ihdffJG+ffuSmZnJlClTui1+QRC6h6IqC/mldWQkRrLhcDGPLc4h1KxotOsuZQR7Cq01RwodArlZBhkgJcYQk4Ge0OaN/NI6TCbPJFKoyUR+aZ1roqE3nBP1igIk+rXWPLpoN81NfR2JvS2cDUNy3DrqHTt2zFWCbe3atdTVNZXE27lzJxMmTADgpZde4s033yQp6fzJofoikN9w+/8DrYzRgAjkAHLbbbfx2GOPMXr0aEaNGtXp/bz66qu88cYb/OIXv8But3PnnXfy8ssvd2jb6OhoPv/8c37zm9/w0Ucf8dprr9GrVy+GDx/Oc8895/LwRkVFsXr1an784x/zhz/8gaioKG677TbmzZvH3Llz2z3O+PHjeeaZZ3jllVdYtmwZdrud3NzcNgXyzJkzee2113j22We5//77yczM5LnnniMvL69bBfLYsWOZM2cOn376KX/961+Jiopi7NixZGVlcemll7rG/e1vf2PixIn87W9/49FHHyU8PJzMzEy++93vMm7cONe4F154ge9+97v8/Oc/p66ujm9/+9sikAXhHGNxdgGPLc5BKWhotKO18Ue03vGw6fElOUwdmtxpwdMTnKm0UOUIeJi3DHJM8GaQMxIjsdrsHsusdjsZie00C3Gc0+HCKoqqLD3++by86jBfHC1psbwjsbfFqH7x6MYG8nOyue9H/+Gz1StbWB6VUkyePJn58+d7TChvPrH/fEB1tJyHUmp6R8ZprT9rf1T3oJQqj4+Pj2/t0bOzE0tHfLTnKkVFRfTt25dnn32Whx56yOft33jjDe666y527NjhIciEc5cL4boXhHONoioLVz63FkujvdUxseEhvPGdyUwcEHwz/J18fugsd/xzK2aTYt9TcwgP8ZzE/MLyg/xp7REuHZTIe/d2T8ezrnDPm1+yan8RJgVhIaYOZe1/8K/tLM0549M2/qCoysK7W07w8qrDAEwZlMiu/HJCTSasdnun48jNzSUrK4v/LV1K1opVaKvnzUxycjJz5sxxeYmdjbnOBxISEqioqKjQWic0X9fhDHIgha/Qcf75z38CcOuttwY4EkEQBKE18kvrMJvaniPS1YxgT+D0Hw9IimohjgGSY4xayMGYQTYwPoOrh/fmNwtGtZsNLqqysHJfIeDp/e3uTP/i7AIeXZRDgyPjPSQlmnfunkJZbYPLotPR49fX17N+/XqWLl1KVlYWBw4caDZCEd53KDGDJ/Gze27hodvmt1q96XzGF4uFC6VUOJAMnNVaB2/17wuINWvWsHfvXn77299y0003tSgJVldXR0VF2z3WExMTuzNEQRAEwUFGYqRL7DgxKqQZ/mMw2jUHs70CDJsBwLDUlvYKgBRH/MHabvpgoTH5edqwjgnc/NI6QkwmrG4Tvbvq/W2PoioLjy/J8bhe8svqKKttIDU2okPHPX78uKsE2+rVq6mpqfFYn5iYyIyrZ7G+Lp3wzAmYoww/8uuHTdxVayU1VgRymyilJgAvAFMBMzALWKOUSgX+DfxWa73K71EK7fLUU0+xceNGpk6dyosvvthi/fvvv89dd93lZcsm1q5d213hCYIgCG6kxkZwxZBk1h08i1lBqONRfVxkKPe8uQ2AWSN6BzjK9mmqYNFygh40ZZAr6qzUN9q8ZpkDRZXFSn6pMensIi/+aW9kJEZib2ZN7e5MvyHKPZ82hJnbFuUNDQ2sX7/eJYr37dvXYsykSZOYP38+8+bN45JLLmFnQSW3/n0z9W62n+4W/8FMhwWyUmocsB4oxpiI51JbWusipVQk8G1ABHIAaK8N8Zw5c1i5cmWbY8aOHcuMGTO48847/ReYIAiC4JUax+S2a8ek8fOvjCA1NoKymqZMa15xLaPT4wMVXrtorV01kId4qWABkOLWea6kuiGgneea44wdYHgf711Qm5MaG8FvF4zmwQ92ARBqVt2e6c9IjKShsf3JhCdOnPDIEldXV3us79Wrl8tLPGfOHFJTU1scp6fFfzDjSwb5KeAUMB6IAL7TbP1q4Bt+ikvwM2lpaaSlpQU6DEEQBAGw2oyOZQBzR6W5BFav6DDiI0OpqLNyrLg6qAVyUVU9VZbWK1hAUx1kMHzIwSSQD5wxBHLf+Ajio9rvoudkwYR03tl8nOwT5dxySUa3T9BLjY1g5vBUlu0t9JgYmBBuYu3ata4ybHv37m2x7cSJE5k3bx7z589n8uTJbXqJU2Mj+PHMIby40pgEGO44zoWYPQbfBPI0DAtFtcOD3JwTQFD3wm3eIEIQLgRsNhuhoR3/5S8IQvdz8EwVFquRFRyXkeCxLjM5mp355eQVt2g1EFQcdtQ/NikjZm/Ehoe4WhgHWy3kA6cNgXxRn47ZK9wZ2Tee7BPlnCzvmYYuTtvDpakwMSSXd57+O3euWkVVVZXHuISEBObMmcO8efOYM2cOffr41gD5zisyXQL5z7dPYObw4Lf5dBe+COQIoK1ZXh17PhFAwsPDKSsrw2azXZAzMoULD7vdjtVqJTIyeLI2giDAzvxywOjKlhbvmaEb5BDIucXVXrYMHpwWhYFJ0USEev+barSbDudkeV3QVbI46MggD0/zXb4Mc3iu3W0a3YHVamXDhg3877U/U37oS/59No9/Nxszfvx4l5d4ypQphIR0qv4CAHERoSREhVJea3U9HbhQ8eVdPApMbGP9TKClCzyIiIuLo7S0lNOnT5OWliYiWTiv0VpTXFyM3W4nPj54H9MKwoWIUyCPy0hAKc8JWAMd2djc4prmmwUVzgl6rfmPnaTEGgI5mDLIWmv2nzEqWAzvRAZ5iKNqR0FZHbUNjUSFdV6UNufkyZMsW7aMpUuXsmrVKiorKz3Wx8fHM3v2bJeX2N/2yf6JUZTXVlBQVtf+4PMYXz7Rd4FfKKU+AHY4lmkApdRPgbnAT/wbnn+JjIykd+/eFBYWUl1dTXh4OCaTKdBhCUK30NjYSENDA3FxcR1u3y0IQs/gEsj9E1qsy3QTyM6Wv8GIs8RbaxUsnARjN71TFRZXhrSjE/TcGeZ2zkeLarrkFbdarWzatMnlJfbW1TU0dRAJwy7hw6d/wBVXXN6lLHF7ZPSKYndBBSdKgtvi09348g6/gFHWbTlwAEMcv6SUSgH6ACuBV/0eoZ9JTEwkMjKSqqoqLBYLHe0kKAjnGmFhYSQmJkr2WBCCjIo6K0cc2dfm/mNoEsiVlkZKaxpIivE27SewaK1dGeTWJug5cVayCKZayAcd2eNQs2JQinf/dFskxYSTGB1GaU0DhwqrfBbIp06dYtmyZWRlZbFixYoWWeK4uDhmz57NvHnz2KkH8MlhC9OHpTB9+mSfY/WVjEQjoZJfJgK5Q2itG5RSs4AfA7cBFmAYcBh4Efi91rr1nplBRGRkpHgyBUEQhICwu6AcMCa3jUlPaLF+oNuEt9zimqAUyGer66moswIdsFgEYTe9/aed5eliCTV37knykNQYtuaWum4U2qKxsZHNmze7utft3LmzxZgxY8a4vMSXXXaZa3L1vN+vByxM6N8zLcedZd1EIPuA1roReMnxIwiCIAiCj+w8UQ4YmdeY8JZ/hmPCQ0iNDaeoqp5jxTVMGhh8XU6dFSwUxsSutmjKIAePQHaWeOuM/9jJsN4OgdzKRL0zZ864vMQrV66kvLzcY31sbCyzZs1i/vz5zJ07l379+rXYR019oyvbPd6LHac7yOhlZJBPlVtotNkJ6eQNxLlO95lYBEEQBEFogfsEvdbITI6mqKqevCCdqPfRtnzjPwpmvfQZz9wwutV6wMHoQT7YhQl6ToY6Juo5M8iNjY1s2bLF5SXesWNHi21Gjx7tqkt8+eWXt1uCc1dBOXYNSnn3q3cH/R0WC5tdc7rC4rJcOCmqspBfWkdGYuR5XSPZl056d7QzRAN1GPWQsx3ZZkEQBEEQHGitOySQB6VEsyW3NCgrWRRVWfhk92kAtAaL1c7jS3KYOjTZq2ByZpCr6huxWG2tloTrKeobbRw9a7yvnSnx5mRo7xhsNWXs37Oam7b9hdWrVlJWVuYxJiYmhlmzZjFv3jzmzp1LRkaGT8fY4XjaMCQlpt1Mvb/omxCJUsZne6K01kMgL84u4JFFuwk1m7DbNc8saP3G6FzHlwzyGziqVjhwTqttvkwDJUqpJ7TWf+9aeIIgCIJw/pBfWkeJo510WxnBgUnBW+otv7Rl+a9Qk4n80jqvAjk5xrObXvOMZE9zpKgam92QLr5mkG02G1u3bmXp0qX8939LKdiRDcBHbmNGjhzpyhJfccUVhIWFdTrWHScMwd1T/mMwOvX1jY/kZHkd+aVNPuSiKguPLc7BatNYHU3X2roxOtfxRSDPAp4FEoG/AAcdy4cD3wOKgd8Cg4EfAn9RSpVqrRf5L1xBEARBOHfZkW8Inugws+sRvTeclSzySmqw2zUmU/CUestIjMRu96wAZbXbXZO7mpPi1m66uDrwAtnZIKRXVCipse1PgCwqKmL58uUsXbqUFStWUFpa6rFehUZwyRVX8p1bFjBv3jz69+/vlzi11q4Mck/5j52k93IIZLeJevmldTS/DNu6MTrX8UUgXwGEA6O11u5TGz9RSr0KbAJGaa2fVkr9FdgFPAiIQBYEQRAEYOPRYsCovWtuQ/Q6S49ZrHbOVFromxA8lZeSo8MxKbBpiAw1oYFnbhjdqkiKDg8hMtRMndUWFD7kpgl6cV5rTNtsNr788kuXl3j79u0tSsKOGDGCefPm8WXjAPJCMlhwzXC+N3e4X+M8UVrretowYUDPZZDB8CFvyS3lhNvTgozESKy2jt8Ynev4IpDvBv7QTBwDoLWuVkq9gVEC7mnH67eAn/onTEEQBEE4t1mcXcAH2woA2FlQzuLsglb9mxmJUZgU2LVhswgmgXy2uh6nTnrmhjFcMTSp3QxiSmw4J0prg6IW8i6HB9xd2J09e5bly5eTlZXF8uXLKSkp8dgmKiqKq6++2lWGbcCAAYBhMTi+5YSrqoc/cWaPY8NDGJLSdik9f+OqhexmsUiNjWBQSjSHCqsJNSvMJtXmjdG5ji8CORVoy1kfAvR2e33Kx/0LgiAIwnlJUZWFxxfn4ExE2uy6Tf9meIiZfr0iyS+tI7e4hiuGJPdwxK3jLppmj+xNtJdSdc1JjgnjRGltwDPIi7ML2JJbitZ2/v2/NRxf+SZ5O7/gyy+/bJElHj58uMtLPG3aNMLDW9oxhjlqQB8u8l7qrSt8ccR42jCib1yPW2xctZDdPmu7o6oFwPenD+b2ywact+IYfBOwh4C7lVJ/0Vp7tHxRSsVjZJgPui3OBIq6HqIgCIIgnNvkl9a1EDnt+Tczk2NcAjmYcPpSk6LDOiSOIThqIe/PK+CHT/2BqsNfUpebjb2ukg/c1kdFRTFz5kzmzZvHvHnzyMzMbHefQx1dBE+U1vpcoaOtcmmLswv4aLvxtGH78bI2nzZ0B85SbyU1DdTUNxIdHsLRs9Wu9tw3TEg/r8Ux+CaQnwI+AA4qpV7HEMwAFwF3YmSYvwGglDIBtwBf+C1SQRAEQThHMfybns1m2/NvZiZF8TnBV8nCWcUi3YfJdoGohWy329m+fTtZWVlkZWWxZcuWFlnisKR+3Pi1r3LnzTdw5ZVXEhHhm+gb2tvIIGsNR89WM7Jvx1pOf7gtn8eX5BAWYsJm1x51pJ3VIpyRNrbztKE7cDYLAeOGaHifOLIdFTV6RYUyMCmwEy17Al9aTS9SSt2K0Vb60WarTwO3u1WsMAPzgLN+iVIQBEEQ3DjXmhWkxkYwe0Qf/pdzGpMySmm15990VbIIOoFsZJAzenXcF91TGeSSkhJWrFhBVlYWy5Yt4+xZTxmiQsKJ6D+ayMGTiBg0idiUvrz88FWdvoZSYsKJjwylos7K4cKOCeSiSguPLNqNXeO1XFp+aR3N5w72dLWIlNhwwkNM1DfayS+tY3ifOLeKGr28Tm483/C11fT7SqmPgIkYFgqAPGCb1trmNs6Kp91CEARBEPzCmxvzeOrTfYSHmLBr3WYXt2AiLtL4kzs5M5E/fHN8u2In0zEx60RpLVabndAgafnrtFj4Uq7NlUH2s0C22+3s2LGDpUuXurLEdrtnpn7o0KHMmzePiVNn8vPNNkyh4USGmtHoLk8yU0oxNDWGbcfLOuxD/seGXJpVyfMQwBmJkTQ0+va0wd8opchIjOJIUbXrhijbVZM5ocfiCCQ+T6JzCOGtjh9BEARB6DGKqiw89ek+bHZNbcO51azgeIkhNMZmJHQo1kxHs5BGu6agrM6VUQ40TouF+2P49nBlkP1gsSgrK3NlibOysigq8pzuFBERwVVXXeXyEg8ZMgSAF5YfxBR6hNTYcF65bQIDkqL8cs0M7R3LtuNlbM0tpajK0uY+d+aX8/qG3BbL3QVwVFiIqxNbRKhxUxSIahH9HQL5RGktlRarq6X2+B5sWhJIOlVlQikVBSTR1E3Phdb6RGeDUUo9DDwH7NJaj+vsfgRBEITzk/zSuhY+UruG3fnl9IoOD2rLhVMgO7vktUe/XpGEmhVWmyavuCYoBLLVZud0hUMg+5DRdGaQaxps1DY0EhXWcflht9vZuXOnSxBv2rSpRZZ48ODBzJ8/n8umX03/UZcwtG+ix3Vgs2vXpLebL8ngkoGJHT5+e9Q2GBPXth0v48rn13p9olFUZWHfqUoeXZSD1a5JiQmjtMaKTWsU8JvrR7niXb2/EJuGUJPi73dM4qI+sQG5pp0WmoKyWnbll6M1KGXc4F0IdPgKdUy8exij1nGfNoZ2qsm6UqoP8HMguMxWgiAIQtCQGBXa4vF0Q6Ode9/eTlRYCI12e1BaLuobbZxyCMsBHbQmmE2KAUnRHCmq5vPDZxnZLy7g4v90ucX1/vuSQXbvWHfwdBXj22l8UVZWxsqVK11e4jNnznisDw8PZ8aMGa66xEOHDmVxdgGPLc5B7dyF1vDbBU3XweeHznKm0ihRdtPEjA7H3R5FVRayck4DxkQ9i9Xe4onG4uwCHl9sCGObXRNqVrz73Us5U2HhW//cigaG9Y5z7TMrxzjX6RelMG1oit9i9RWnheZEaa3Lf3xR71hiOli55FzHl7N8FngI2IvRHa+k7eE+8yywDTABCX7etyAIgnAecKKsqbNXTLiZ2nobdowscnW9kckLRsuFkfk2/t/fhwoA4Q7f8Tubj/PvrScCLv6d/mOl8Kl5ibOmL8DNf9vMszd6nofWml27drm6123atAmbzeaxj0GDBrnqEs+YMYOoqKb3sajKwuNLcqh38+4+trjpOnj/y3wALh+c5NP73x75pXWEhphocIvV3U/sjMvSzFMcHxXKkNQYhqTGcKSomo93nmR0ejw19Y2sPWhYRuaNSvNbnJ2hqVlInct/fKHYK8A3gXw7sExrPd/fQSilJjv2Pwl42d/7FwRBEM4PvswtBeCiPjE8c8MYymoa+MG/ttPg1gK3p2f8d4QTpcbD0TCzibT4jgnLoioLBwqNiV9Wm8Zq6/lyX81xTthKi4sgLKRjkwaLqiws/O9e1+sGm5FlHZMays7N613WidOnT3tsFx4ezvTp011e4mHDhrVaPSG/tI5QkwkLTUK00aY5WlSNSSlW7S8EDHuFP8lIjMRmb739sre4IkLMruvz+nF9eWHFIT7ZdYrH51/MmgNF1DfaCTUrrhnRm0DifEJQZ7Wx6aiREx1/gUzQA98Eci/gP/4OQBlX+x+BN7XWO9sqHaKUKm9ndx0rQCgIgiCck2zNMwTy5YOTmTigF0VVFodoahIpPT3jvyPkFRvCMj0xEnMHu6Lll9YRYlIeAizQ4t+ZQfalBrJTJNZpG9azudQd287Z3GyG/3ZfiyzxwIEDXbaJq666iujojvmuMxIjsTbzJdu05tV1RxmTnkCjXRMdbmbOyLYcor6TGhvBMzeM5qEPd2HXYFae7ZczEiNpaKP+9dfG9eOFFYc4W1XPxqPFLHXYNaYNTSE+MtSvsfqK+3fImZmfIBlkr+QA3ZHvvwMYAVzfDfsWBEEQzhPqG23szC8HYLJjklVqbAS/XTCaBz/YBUCoWQVkxn97nCj1bYIeGAKl2XzEgIt/XytYVFRUsGv9MvI/eYPqI9uxVXu6M8PCwrjyyitdoviiiy7qVI3d1NgIfvGVETyxZA8AJmXYbtYfLmb9YcPeYbHaWZpz2u8WlQUT0jlcWMWfPztGZnKUx/5TYyP47rRB/GntEQDCm9W/zkiMYtKAXmw7Xsa/t55w2Svmjw6svQIgNiKUXlGhlNVaAYiLCGFQEEwU7Sl8Kar4JHCfUspvzyeUUrEY3uNntdan2xuvtU5o6weo8FdsgiAIQnCRU1Dhqg87ya0KwYIJ6Vw+OAmA68f1C7oJegDHSwyLRX8fMq+psRH88qsjXK/DzO03F+lummogexfpWmtycnJ4/vnnmTFjBsnJydz9rVup2LnCJY7NcanMuvF2PvnkE0pKSli5ciUPPPAAw4cP71IDitH9mh4iL7//Su66fKDHepujI11RlaXTx2iNSwcnA3CsuMZV1cKJMxOcFh/B+keuanF9fm18PwCW5pzBYjXsFbMCbK9w4n69ju/fq0W79PMZXzLIE4HjwD6l1BIgF7A1G6O11r/2YZ8/BxowuvMJgiAIQqs47RWZydGuurpOxvdPYOPRkqBry+ykqcSbbxPEbr90AC+vOkRxdQOPzL0o4OLfWwa5srKS1atXu7zEBQUFHtuEhoZy5ZVXMmrKdD4oTCY0KYO/PDSDQY5GKP7C+dnHR4YytHcsXxmTxjtbjmPtAX/6GIc4t2vYd6rS4wbO+dTj0kFJXo977eg0nvxkL40OK80lAxMDbq9wkp4Yxa4CI/c4rLd/P69gxxeBvNDt/7e3MkYDHRLISqk04H7gF0Bvt7vGCCBMKTUQqNBal/kQoyAIgnCesi3P+HNwycCWPsgRaYZA2X+6ErtdB1Wmy2bXrszrAB8sFk4u6hNL8ZESV5myQFHXYKO4uh6tNQ1nj/O73/2brKws1q9fT2OjZ9Y0IyPDZZuYOXMmsbGx2O2a1U+uoKq+kd0FFX4XyE6f90CHDSAjKQqzUlh7wJ/eKzqM/olRnCitZVdBhYdA3uGoADGulfrBidFhXNQ7hr2njQmZX+aVsji7IOA3QwC19U2f6xsb87g4LS4o4uoJfBHIme0P8YneQBhGY5DnvKzPdSx/1M/HFQRBEM4x7HbNNkcG2VuThxF9jTqyNQ02TpTWukRSMHCqvM6VxexMibGhqbF8caTE1cksEFRXV/P2ok8pWfYmdce2c+vzZz3Wh4SEMG3aNJcoHjFiRAu7hMmkGJ0ez8ajJewqKOd6h7XAX+QWG+9PpuM9To2N4JkFo3l8SQ6hJhNWR43s7rKojEmP50RpLbsLyl3LiiotnKowbmxaE8hFVRYOuX22wVCtxBmX078dTHH1FB0WyFrr434+di5wg5flTwPRwAPAIT8fUxAEQTgHOVhYRaXFyGZNzmwpkAckRhEVZqa2wca+05VBJZCdE/RMCtJ7+Z69HJxqZFqP9KBA1lqzf/9+V13i9evXY7VaPcakp6e7SrBdffXVxMXFtbK3JpwCOafA/1OGcks8M8hg+NOnDk0mv7Su27ssjkmP59Pdp9ntdm47HPaKsBATF6d5f3/yS+uICDFjtTVlawNdrcQZV3iIicYG7zWez3c622p6CEYGeI/WulNXuWO7j73s+36gUWvdYp0gCIJwYfKlI3ucGhvudaKbyaS4OC2O7cfL2HeqMiiqADhx+o/T4iMJD/G92exQh0AuKKvzuU2zL1RXV7NmzRqXl/j4cc+8mNkcQki/i+k36jI+fvZHjBo1yudJdWPTEwDYc6qCRpudELMvtQLaJs/hQW7ekjs1NqJHBN0Yx7nlFtdQUWclPjLU5T8e2Teu1brR3krUBbpaCThqPOvWazyf7/j0LVNKfQX4PTDQsWgWsEYplQpsBB7VWn/k1wgFQRCEC56tjgYhl2QmtirKRjgF8unKngytXZwVLAYmd66Dm1MgAxw7W8Oofv4p+a+15uDBgyxdupSsrCw+//xzGhoaPMb07dvXZZvIbkznneyzXD40mdGjR3fqmGPSjdgtVjuHi6pbzar6SllNAxV1Roa7uUDuKUb1i0cpo+X0npMVXDEkmZ2OFs3jM1qvH+yspdxTVpCOEqxx9RQdFshKqRnAEmAn8CZuk/a01kVKqaPALUCXBLLWekZXthcEQRDOL7TWrgzyZC/+YydOsbXvVLAJZCOD3D+xc8ItKSbcVY/2cFFVlwRyTU0Na9eudYnivLw8j/Vms5krrrjC1dJ59OjRrhuS5W9vA5paEHeGfgmRJEWHUVLTwO6Ccr8J5NySpuolgbLXxISHMDjFaB29q6CcSwclufzI49rpQNeTVhBfCNa4egJfMsi/BHYBUzC66i1stn4TRtMPQRAEQfAbO0+UU1hZD3ifoOfEOVHvTKWFkup6kmLCWx3bk+Q5M8idmKDnZGhqLFvzSjlc6JsPWWvNoUOHXF7izz//nPr6eo8xaWlpLi/xNddcQ0JCgtd9+dokxBtKGRP11h08y+6CCm6+pNO78sBpr0iKDiMuInAl0sakx3OkqJqcggoOF1VR4/Dvjm9lgp47PWUF8ZVgjau78UUgXwL8Umttb+XxVgHg3x6OgiAIwgXN4uwCHlm02/V636kKlxBuzkW9Y10d1PafrmLq0MALZK21a5LegC4I5MGpMWzNK+3QRL3a2lrWrl3rEsW5ubke681mM5dffrlLFI8dO7ZDXuL2moR0lDHpCS6B7C+cAjnQkzPHpiewOPskuwsqXPaKpOiwTk3OFAKLLwLZBNS3sT4Zo+mHIAiCIHSZoioLjy/J8Wj08PP/7OHKi1K8ZrQiw8wMcjzi3ne6gqlDk3syXK+cra6n1pFF7KzFApp8yK0J5MOHD7tsE+vWrWuRJe7Tp49LEM+aNavVLHFrVNRaqXJUEelKBhlgrMOHfOBMJfWNtk5NXGyOq4JFJ+pM+xOnx/pkeR2r9htto8dlJHSpQ6AQGHwRyPuBacCrraz/CoYFQxAEQRC6TH5pHaEmExaaZvi3V2ZqRFqcIZCDxId8wiHcoGsZ5KGOLmZ5JTXUN9qwWxtYt26dSxQfPXrUY7zJZOKyyy5zeYnHjh2LydT5ihHO7DF0zYMMRqk3MOrq7j9d1Wp9YF9oqmDRtdi6ysVpcYSYFI12zZoDhUDr9Y+F4MYXgfwa8Ael1CrgE8cyrZSKAp4FLkM8yIIgCIKfyEiMxGrzrfzViL5xfLLrVNBUsnBO0EuOCSc6vPPl2YakxmAtO0Xdse3Mmv0yX27agMXi2Vmvd+/ezJ0715UlTkxs3a/tKwUOgRwdZqZXVNc8vqmxEaTFR3C6wkJOQXmXBaTWOmgsFhGhZi7qE8veU5U4Okczvn/rFSyE4MWXRiF/VkpdAfwd+H8YbaX/DSQBZuB1rfW/uiVKQRAEocsUVVnOqdnoqbERPDBrGL/NOgBAeIip3TJTIxxVEY6ercFitRER2vXH913heBcm6NXV1fHZZ5+56hKfOnwYgPWO9SaTiUsvvdRlnRg/fnyXssRtsd/RBjktPsIvdoEx6fGcrrCwq6CCb3VxXyU1DVQ5WiIH2mIBhsd6r+MJhlIwJsM/ZfmEnsWn21mt9e1KqUXA7cBwQAFbgLe01ou6IT5BEATBDyzOLuCxxTmEmBQ2rXnmhtEsmJAe6LDaZVCKYS0IMSs++9lV9IlvW9g7y4bZ7JpDhVWu5g2B4rhjgl5HW0wfO3bMNblu7dq11NXVeaw3RcUz4Yqr+Ol3bmb27Nl+yxK3dfO0OLuAP609YsRXXMPi7IIuXztj0hNYvrfQoy1zZ3FmjyHwGWQwPNb/3mr8f0BiVECragidx+fnPVrrJRj1kAVBEIRzgKIqC48tzqG+0e6aaf34khymDk0O+kzyoUIjczk0NbZdcQyQEhtOamw4RVX17DtVGXCBnNfO5DGLxcLnn3/uEsWHDh3yWK+UYsqUKcybN49DYUNYXxbL5LH9uOWWCX6LcXF2AY8uyiHErLA3u3lyTpS0OfwCdu2fa8fZUe9wYTV5xTUdFrbehPwxh0BOjQ0npgs2Fn9RVNVkfckvrfPLDYXQ83T5SlJKJQO9tNaH/RCPIAiC4GfyS+swNXss3t5kt2DBWbVhWO+YdkY2MaJvHEUHz3a7D7kjlpUTDouF+wS93Nxcl21izZo11NbWemyTnJzs8hLPnj2b5GSjGsdfPzvKhqwDHPGxFnJ75/Do4hwabHYcxTY8BHB3XTvOrK8GZr/0Oc/e2P4TDUPI78ZkMuJxCvlg8R+D8X6+urZpwqRN63PmZlTwxJdOencAU7XW97otexb4meP/m4G5Wusqv0cpCIIgdBpvk93qG9ue7BYsHC5yZpB9EMhpcaw7eJatuaUUVVm6RZgszi5o0YK3ucCrqLNSVmtFN1o5nL2RB9/aRFZWFgcOHPAYp5TikksucbV0njhxImZzS+/0EMd7kFtcQ6PNToi5637j/NI6mjuK3QVwekIkdVabx/r2Jkq2R1GVhaeX7nO9brDZ2xWRzqcgDTYNjrJ/zm2cjVgyg8B/nF9aR6jZhKWx45VXhODElwzy94CDzhdKqUnAw8DnwAHgbuBB4El/BigIgiB0jdTYCEb1i2dnfrlrWd+ECFKCpNNca9jt2pVBHto7tsPbVVmsABw4U8WVz63lmQX+9Vs7bQcWq91Vgq65wMvLy+NnL71B0dIsLMd38zOrZ8WJpKQk5syZw/z585k9ezYpKSntHndoqvEeNNjsnCitdfmzu0JGYiSNzW6e6qw2lwBed6gI7ajGEBlqRqPbnSjZHp0p35dfWofW2mOZc5vcYoeNJQgyyBmJkVjtvlVeEYITXwTyEOBDt9c3AaXAbK11g1JKA99ABLIgCELQUekQjZMG9GLb8TLySmpZvreQuaOCtwFqQVkdFqshNjqaQS6qsvDBtgLXa0tj+9lJX/Em8LBZWfTfZRzN3sDSpUvZv39/i+0i+g7jh9/6OjfdcB2TJk3ymiVui369IokINWGx2jlSVO0XgZwaG0FidBhnqxtQGJYHrTXVlkbMqt5VQWTuyN5898rBfqmA0hkRmRYfYWSP3bA02sjoFemqFBLoGshgvJ/P3DC6xdMFyR6fe/gikOMB976QVwOrtNbO7nnbMKpbCIIgCEFEbUMjuQ6f5oOzhvGPDbmsOVDE88sOcPXFqYT64VF9d+C0V4SZTfTvYHMK5yPu+m58xO20rDRWFlF3bDt1x7ZjydvJD5pliU0RsURkTiBy0EQiMyeQkJjMN78zmYkDOlcX12xSDEqOYd/pSg4XVTN7ZNfPpdJi5Wy18Wf80XnDef2LPM5UWnjg/Z0kRIVSXmslLiKEX18/mpRY/zxxcIrIRxftdoneJ68b2ebn4/70w9lOPDkmHLvG1akwGDLIAAsmpDN1aPI5VVJRaIkvAvkMMBRAKZUCjANed1sfA9habiYIgiAEkv2nq1yPyUf0jeORucNZd7CIY8U1/G75Qe6ZlhmUf8QPOSajDUqJ7rDfNiMxksZuesTd0NDAhg0byMrKovKj/3A6r+Xc9LA+Q4gcNInIQRMJSxuGMjVlif0Rx9DehkDeklvCTZPSu/y55RQYeS+l4LZLBzCibxzfem0ruwqa8mGzR/b2mzh2smBCOiP6xjH3ZaOq8/A+cW2Of3fLCQCmDkniurF9eXhRDqcrLPx53RHXmAFdaOXtb1JjI4LyOyV0HF8E8hrgh0qpUuAqjCcx/3NbfxFw0o+xCYIgCH7AWc2hX0IkCVFhJESFMWlAIlvzSvn758d4c2Mev/WzT9cfuCbo+eA/dmYnf/bRbmx2jUnRpUfcBQUFrhJsq1atorras4KEKSKGuXNmczr2Ys4mDMcc3ZQdNisIMZsIM/vvUXu9Y8LchsPFXPn82i7Xs3ZmZoemxhATHsJFfWIxK6NWtpNPd53m4bnD/S74hveJo3dcOIWV9ew7XcnYVjrq5RXXsOFIMQB3XZHJ1Rf3ZuX+IlbuK+TNTccBw4IRGRbYpjDC+YUvAvmXwOXA847XT2ut8wCUUiHAjYA0CxEEQQgy9p0ysoHOJhpFVRZ2ORo0aIyKFsFYiuqwI4PsSwULMLKTRVUWns06SFJ0uE8C0mq18sUXX7hE8Z49e1qMmTBhAr2GTWaPeRCXXTqFJT+6kk1Hi/nWa1tptDcJy6iwEF66ZRy9osL88qi9qMrC6gNFgGExsFi7/rntcghkZ13i/NI6IsNMVNc3PRAONXdfFYaRfeMprCxi76mKVsf8+0sje9w3PoIZF6UChlVo5b5C15i+8TIJTvAvvrSaLlBKjQRGABVa6xNuq6OAe4Fdfo5PEARB6CL7HG1vR/Y1BHJ+aR1h3ezT7SruFSx8qYHsZEpmEgBnq+spqa4nqY2KHSdPnnTVJV61ahWVlZ71k+Pj45kzZw7z5s1j7ty59OnTh3vf2saRfYUM75sAwODUGELMykMgW+12xqTH+3VyYJjZhNXmJl478Lm1Va/ZeaPkzN4aFhXPyXDdWYVhRFocaw4Uua7R5tQ32vjIMeny5kv6Y3bUQL44LY5x6fHsdFhBduaXS0MOwa/42mraBuR4WV4J/MdfQQmCIAj+odFm58AZw6owwiGQvVYRsAVXKaqT5XWu+rtDUjtusXAyvE+cazLX/tNVTB3aJJCtViubNm1i6dKlZGVlsXv37hbbjxs3zlWX+NJLLyUkxPPP5WFn+TlHbD1RvSAjMdLD+gDti1dni3GlQGs8rDRnKiwUVhq9Fcc5BHJPV2FwXpP7T1dhs2uXAHby4bYCSmoaMCm4+ZIM1/KiKgv7zjS1XZCGHIK/8aVRyBBgiNZ6mduyKcDPgUTgTa313/wfoiAIgtBZjhXXuDLFzgyyUwQ5208D/HDmkKASFk7/cahZMTDJ9/JdkWFmMpOjOXq2hn2nKxgU3cCyZctYunQpK1eu9JolnjVrFvPnz2fu3LmkpaW1um+L1eZqTjHMzR/d3dULnJ/bQx/uwq4Nj7M38VpUZSG/pJZDRVU8sWQP7glhdxHp9B+HhZi4qE/PnYc7zmuyzvGeDnYrXbc4u4Bf/qfJ4rLxaLFL3OeX1hFuNtEQxE9BhHMbXzLIz2EI4WXgajGdhVG9og74s1KqSGv9sb+DFARBEDqH09sZFxFCv4SmTOOCCelMHZLMvN+vp6SmgcjQ4Jrg5PQfD0qO6VTHuMbGRhIqj1L22XJ+/v5uvpd3sMWYsWPHMm/ePObPn8+ll15KaGhoh/Z9pKjaVRVkWB9P+0d3Vy9YMCGd4yW1/H71YdISIltYCox2zDk02u00c0oAEGJSLhHptFeM6hvXotRfT1VhyOgVRUx4CNX1jew9VekSyM7Oec5zsGtPcS8NOYTuxpffOpOAVW6vvwnEAROAFGAL8BP/hSYIgiB0Fae3c0TfOJTyfHydGhfBVcONSU9fOKoEBAtOC8MQH/zHp0+f5vXXX+cb3/gGKSkpLH7qHio3f8hZhziOjY3lxhtv5B//+AcFBQXs3LmT3/72t0ybNq3D4tiIzchuJ0SFBqQb4bShyYDRSKWkut61vKjKwuOLc2iweRfHYEzsc4pI1wS9VqpH9AQmk2KEY/Kouw/Z6JznOdaZIYambHpEqInY8BAiQk3SkEPwK75kkFOAU26v5wJfaK33ACil3gOe8GNsgiAIQhdxlngb2Tfe6/qpQ5L5aHsBW3JLaWi0ExYSHE1DDhcaInRYG/7jxsZGtmzZ4vIS79ixo8WY0JSBRA2exIdPf58ZV/omhFvDWZ95WGpsi5uOnmBUv3hCzQqrTbMzv5yrL+4NGKLS1MzDG25WaBQajdWmiYsIISk6HLtds9sxwW1cAAUyGDdvW/NKXdcqQEYvoxmLO80zxNKQQ+hOfBHINUACgFLKDEwF/uC2vg4joywIgiAEAVpr9jozyGnefz1fPsSo9lDbYGNnfjmTMxN7LL7W0Fo3TYJrlkEuLCx0eYlXrFhBeXm5x/rY2FiuueYa5s+fzyVTr+JrbxitkvtcNNEv4hiaxHvz2HqKiFAzI9Li2FVQQfaJMpdAzkiM9KhMAqBMiiU/uJwjhdX833s7Ka21su5gEQOSoqiubwSaSrwFCudEvX2nKtBao5TibHU9zgRyVJgZu9ZeM8TSkEPoLnwRyHuBO5RSbwE3YXiPV7qtHwCc9WNsgiAIQhc4XWGhvNYKwMh+3gVyamwEw3rHcKiwmg1HioNCIJ+qsLjaBw9KimTjxo2uMmzbt29vMX7UqFHMmzePefPmccUVVxAWFuZalxKby9mqevadrmB0uvcsuq+4Msg+NDDxN+P79zIE8vFy1zJDLIZzusJCmFlhMimeuWE0I9LiGZEWz4fbC1h/uJi3Nh3nq2P7AhAfGcqATkyC9CfOm7fi6gbOVtWTGhfBf3edBgzR//I3xpGRFCVCWOhRfBHIv8Mo5VbkeL0DWO+2fjaQ7ae4BEEQhC7izB6HhZg8qgM054ohyRwqrGbjkWIenDWsp8JrlS37jlG9ZzX1x7Zzxd/3UFpa6rE+JiaGq6++2lVxon///q3ua0RaHJ9VnW21zq6v1DY0kl9WCwQugwwwvn8Cb2w06hg7y6OdLK/jdIUFgF9+ZSSzR/X2EJV3XDaQ9YeL+ezQWewOg+/YjISA2ETcGdY71mUZ2XuqkpTYcP67y3B03jCuHxMHBv6mTbjw8KVRyP+UUjOBrwEVwJ+0Nr5hSqkkoAB4q1uiFARBEHzGKQov6h3bokqBO1OHJPP6F3nsyC+nymIlNsI/VoSOYrPZ+PLLL11e4m3btrUYM2LECFfFialTp3pkidtiRN84Pjt01sPf2hXcK1hcFMAM8oT+Rkvr2gYbB89UMaKv0XADjMmDt0zOaFH9Y+bwVPolRHKyvI71h41JmeP8lFXvCmEhJoakxrL/dCX7TlcSHxXKyXJjMp4z0y0IPY2vjUI+Bz73srwEWOCvoARBEISu4yzx1pr/2MmUQUmYTQqbXbM1t9Tlae0uiqos7D6cz6HtG/hi3SqWL19OSUmJxxgVGkHGyMk89r1vMm/ePAYMGNCpYznPff/pKux23WISm6847RVJ0WFtdufrbtJ7RZIcE05xdT078ssY0TeO1fuN1sszhqV4LY1nNim+ddkAns064Fo2ICm6x2JuixFpcew/XcneUxUUOypzDO8Ty9AA3oQIFzY+CWRBEATh3CHnpCGQ+ye1XRs2JjyE8RkJbDtexoYjxd0ikG02G9u2bePFf77PJ5/+D8upw4BnHa/hw4czdOI0ttoGEN5vJKFhYaROHsOAAZ1vH+ycAFZdb1gjfBGE3lo0B3qCnhOlFBP6J7BiXyHZx8u5YXw/Nh41bjLa+vxunpTB75YdwOZ46x9fYnTZC3SL5pF941iUbVyz2/LKAMkeC4HFJ4GslOoF3A1MAXrRso6y1lpf7afYBEEQhE7yzqbjLj/q71cdIS2+ZVMJdy4fksy242Ws3lfE92cM9suEqOLiYpYvX05WVhbLly+nuNiz1rIKDSdqwFgevPtmLpl6NWEJqXz/X9lEOMRbo73r7YMHJkUTGWqmzmpj36nKDgvkxdkFPL44h1BzU7vlBRPSOeQsPxcEmc3x/XuxYl8hO/LL2HC4mIZGOyEmxZXDUlrdxmiuoXDenNQ32oOiRbPzRsZZ5xjgK2Na72YoCN2NL62mBwBfAH0xPMhxQClNQrkYoxScIAiCEECKqiw89ek+1+sGW/siyO7oLHGirJZpz63ltwtG+5xVtNvtbN++naysLJYuXcrWrVvRzbo9hCamEzFoIpGDJhGRMRIVEsa/KhX/XnoSq72gxT672j7YbFIMT4tlx4ly9p2uZN7o9kVXUZWFRxcZDTcsjrJpzvfPabEIhkf/E/onAHDsbA2Ls08CcMnAROIjW/eQ55fWERFqosZRJQSCo0WzUyC7XqfFBo39Q7gw8SWD/DRGHeSrgRyMahY3A5sxGoTcAkz3c3yCIAiCj+SX1tG8MEFbIqioysI/1h9zvfYlq1hSUsKKFSvIyspi2bJlnD3rWe0zMjKSmTNnMm/ePC6ZNpNb/n2sRZc3m11jwzv+aB88Ii3OEMgdrGSxYk8hDc2aVISaTBwqrHZNHhuWGliLBcDo9HiXd3zZ3jMAXH1xapvbZCRGYmt20xIMLZrjIkJJjAql1FGW8FBhNYuzCwJu/RAuXHwRyFcDf9dar3VUrQBQWuta4Aml1EjgOeA2fwcpCIIgdJyMxPa7kLmTX1pHqNnkypZC64LabreTnZ3tqku8ZcsW7HbPYw0bNsxVl3j69OlERBj7sNrsmE252G2aiFATNrvGrg2B7MTZ+S08pMna0NXMpjM7uTO/nKIqS6v7K6qysHT3aX67dH+LdfWNdhoam2R8MFgsosJCuDgtlj0nm4R/e/5xZ4vmx5fkEGry33vcVYqqLJTXWV2v/WGvEYSu4ItATgL2OP7vvIrdf9uuBH7V0Z0ppSZhZJ4nAKkYto2dwFNa640+xCUIgiC4kRobQUKkkY0LDzGhFG2KoIzESIc3tQl3QV1WVsaKFStYunQpy5Yto6ioyGNsREQEV111FfPnz2fevHkMHjzY63H2n67E6pgd9odbxpOeGMmCVzd6CGRn57e6Brvf2gcXOrzYJTUNrdpHFmcX8Mii3a74okJN2LQh6u0aEqNDOVVu7CclNpxe0R0rM9fdTOjfyyWQ+ydFkpncvi0hGFs0O2/S6jtwkyYIPYEvAvks4KzWXQVYgIFu68PwFMztMdhx/L8DpzHsG7cBnyul5mmtV7axrSAIQo/grZJBsFNW0+B6VP3Lr4xg1sjebcbuzCo6fbda27l5oI2//+H/sXTpUjZv3twiSzxkyBBXXeLp06cTGdn+r39ndYJ+CZHMHtkHwGs2c0Sa/2rzFlVZ+OvnbdtHnJ5jpzgGsGlY8sPL2VNQwaOLczhTWc/zy43yaAMD3HnOnUa3JwUnyywdtiUEW4vmjMRImhfgCwbrh3Dh4mur6bFglKpQSm0FfqCU+gRjkt69wIE2tvdAa/0+8L77MqXUn4FjwE/wbGMtCILQ4zgrGYSYFY127apkEOw4y7uZHOW7IsPM7W5zVWY0Px1SwsMvv0n10W08VVPusT4iIoIZM2a4rBNDhw71Oa7txw2BPGlgL9ey7s5m5pfWEdZOZjK/tA7drORcmNlEXYOdb1zSn9ySWv687iiVdY0AZJ8oDwp/bFGVhUWOyXlgWFXOVVtCamwEzywIPuuHcOHii0D+D/BTpVSk1roOeApYDuQ61mu62CxEa12rlDqLkU0WBEEIGEVVFh5fkmP4cg1ddM6Ij90F5YDhk21NHGut2bVrl6t73aZNm7DZPKfKDRo0yGWbmDFjBlFRnc+caq35Ms9oGT1pQC+Pdd2ZzWzPPuIc02hrfeLarZP785d1R10SOliEaEfE/7lEMFo/hAsXX1pNvwq86vZ6jVLqcuCbgA1Y0hnvsFIqFgjH8Dh/GxiFIb69jS1vZ3eB75kpCMJ5QX5pHaEmExaaxIfNrjlRUhv0f7h3FRgZ5DHN2ghXVFSwcuVKl5f49OnTHuvDw8MZNPoSzsRfzNCJ09j87O2o5uUwOklBWR1FVUaHtIkDEtsZ7T+c9hF3f/GvvjLS4zNsaLS7xG9UmBm71h7Zy6KqeiLDzNQGWWm0joj/c41gs34IFy5d6qSntf4S+LKLMbwO3Oj4fwPwF+CZLu5TEAShS2QkRrYo9WW1ad7efJz0XlGcLA/eLJczgzy6Xzy7du1y1SXeuHFjiyxxZmamy0s8Y8YMDhQ3cOOfN1IInK2qJzXOP+e37biRPY4ND+GiPj1bAWLBhHTGZSRwzYufYdfQJ97znDYeMTrQRYebeeOuyQxIivL4XDMSI7EHYWm0YK1IIQjnA50SyEqpKGCA4+VxR6m3zvIk8FcgHfgWRjY5FKhvPlBrndBOXOVIFlkQBD+QGhvB1yek86+tJwAwK4VNa/6z8xSf7jpFZFgIjW4d1oKFw/mFHPtyDZZj23nojRyKCj2zxGFhYUyfPt0liocNG+aRJR4TEenqPLc5t5Tr/NTu90vHBL3xA3phNvknK+0Lg1JiGJeRQPaJcjYcKeaq4U31gjccMTr8XTE4mUsGtsxuB7MQFVuCIHQPvraaHgG8AFwDOI1tNqXUKuBhrfWeVjduBa11DkbjEZRS7wDbgDeAr/u6L0EQBH/ifPA+LiOBv31rIi+uPMR7X+Zj01BdbxiTA+1F1VqzZ88el5d4wxdfYGs0Yqt2jBkwYIDLS3zVVVcRE9N6k4tQs4lJA3ux/nAxm4+V+E0gb3cI5Ob+457kiiHJZJ8o54sjTS2vtdZsPGq8njo0udVtg1mIii1BEPyPL62mxwPrgBiMChPOPqYjgdnAFUqp6VrrnZ0NRmttVUr9B/i522RAQRCEgLAl17AFzBrRm9S4CG6amM6i7AKPcmCB8KJWVlayevVql5e4oKBZi2ZTCMlDxvLY925l3rx5DB8+3Ccv8ZTMRNYfLmbLsRK/xFtRa+VQURXgWcGip7liSDJ/XHOEA2eqXA1DDhZWUVzd4FrfFiJEBeHCwZcM8u8AO3CJ1jrbfYVSagKwxjFmVhdjigQUEAuIQBYEISAUVVk4drYGMAQjQEZSFCalwK0kWE94UbXW7N271+Ul3rBhA42OLLGT/v37M2/ePA6GDuZoyEC+Pf1iHrx+VKeON2WQ0Sz16NkazlbVkxIb3uFtvdWNzj5RhtZgNinGZSR0KiZ/ML5/gss+suloCV8b148Nh43scVp8BIM60GRDEIQLA18E8qXAS83FMYDWOlsp9QpG/eIOoZRK0VqfbbYsDrgJyNdaF3nfUhAEofv5MtewBISHmBjtqAZheFFH8dCHu9FAiEl1mxe1qqqK1atXu1o65+fne6wPDQ1l2rRprrrEI0aMAGDCr1diqrW6Yu4MY9LjiQg1YbHa2ZJbwlfGdMxmsTi7oIVPd8GEdNcEvZF944gK69Lc8C4RHmJmcmYinx06yxdHivnauH4uu8UVQ5L9VrFDEIRzH19+U1mAM22sP4VvGd/3lVIWYKNjvxnAXRiT9W7xYT+CIAh+Z0uuYS+Y0L8X4SFNtYRvnJjBhiMlLNlxknEZCX6boKe1Zv/+/S4v8fr167FarR5j0tPTXV7iq6++mthYz2oQ+aW1lDk66I1NT+h0LOEhZib078XGoyVsOVbaIYFcVGXhscU51DfaXaXxnP7sbS7/cc+Vd2uNqUOS+ezQWTYcLqah0e6y0Uxtx14hCMKFhS8CeSlwHfBKK+uvA7J82N87wB3A/wG9gHJgM/AtrfVnPuxHEATB72x1CKfJmS1F3ZyRvVmy4yS7CyqobWjsdFa0urqaNWvWuETxiRMnPNaHhIQwdepUV8WJkSNHtpnl3OUo7xYVZmZIausT8TrClMwkQyDntu1DLqqykF9Sy6c5pzwaVoBRN/rgmSp2nDAE8tDeXYvJHzh9xqcqLHy886SrtvHlg5MCGZYgCEGGL7/VHwSWKaU+BJ6nqa30xcDDQCJwa0d3prX+J/BPH44vCILQI5TVNHDgjDGpbMqglgL5ssHJmBQ02IwM5FUXpbYY4w2tNQcOHHDZJj7//HMaGho8xvTr189lm7jmmmuIi4vrcNy7HQ1CRvWN73IptUsd532osJqS6nqSYlr6kBdnF/DY4hwa7RqbXbdYb7VpfvivbBockxoXfrKX8BBTQMviDe8TS2J0GKU1Dby88hAAw3rH+K3esyAI5we+COQijJkpE2jZUtr5m7ioWXZDa60DZzgTBEHoBM6WyKFmxfiMllUX4iNDGZuRwI4T5aw/VNymQK6pqWHt2rWuLHFeXp7HerPZ7MoSz5s3j9GjR3faC+tqENIF/7GTsRkJhIWYaGi0897WE9x0SYaH17qoysLjDkuFO2EhJsLNJuqsNhrtmkpL02TC+kZ7wMvimUyKywcn8enu05yqsADtV68QBOHCwxfx+hbuU7cFQRB8wFt1g2DFaa8Yk55AZJjZ65hpQ1MMgXzYY64xWmsOHTrkEsSfffZZiyxxWlqay0t8zTXXEB/fdUFrt2v2nKx0xN31/UWEmklPiOBYcS0vrz7MH9ce8WiKkl9ah6lZljo2PISXbhlHr6gwMhIjWb7nDL/8z16PPxzB0KJ56pBkPt192uO1IAiCOx0WyFrrO7sxDkEQzmMWZxfwyKLdrsf+wdZ9rjnOiVtTvPiPnVw5NJk/rD7M4aJqjp0uZX/2JlcZttzcXI+xZrOZyy+/3CWKx4wZ0+WKCc1vOI4VV7ual6T36nrZuaIqCydKjXnXVpvGatMe2d+MxEgammWPrXY7Y9LjXeJ3zqg+PP2//R5Z5mBo0Vxl8SyRV1jZonGrIAgXOGJ/EAShWymssPCzj3Zjs2tXg41AP2ZviyqLlb2nDC+vtwl6TqIsZ6nf+Snlh7Yy/KW9WBs8RVafPn1ctolZs2aRkJDQpbjcBfGGw8U8vjgHk0lhtdm5YXw/jhRVu8be9vctPLOgazch+aV1hJpNNNptrmXu2d/U2AhG9o1jV0EFISZFiLllybvU2Ah+uyC4WjQXVVn4fysPeix76tO9XDMiNSivR0EQAoMIZEEQuoWiKgtHi6p5YfnBFhO4guExe2us3l+EXRsTKyYNbBLItbW1rFu3zjXB7ujRox7bmUwmLr/8cpcoHjt2LCaTyS8xOSfDKQUNjXa09vS7fbDNs5OexQ9e34zESOza83Nrnv11Zl7vvGIg9145yOuxgq1Fc35pHaEmk6sUHQT39SgIQmAQgSwIgt9xCjqrzY6X4gZB8ZjdG4uzC/jZR7uNFwreXLYJ+4mdLF26lHXr1mGxWDzGxycm09h3HCkjprDt1QdJSvJ/nd/WJsM1x6TweK+7Kvqc2d+ffrjL1QXPPft7qryOM5XG+3H9uH5tHieYWjRnJEZitbe0hgTj9SgIQuAQgSwIgl9xbxjhRGEIrEaHgnvyupFBI5icFFVZePSDbVQf203dsW3UHdvGj5497THGZDJx6aWXuuoSJ6QPZeaLn6OB0xYz3VFJ19tkuOaEhyhA+d3ru2BCOntOVvDPL/IYnBztYdnYftyobRwZamZ4n9jWdhF0GN0Qg8v2IQhC8CECWRAEv5JfWkezJ/PEhIfw5NdG8uAHuwAYkBQdgMi8c/ToUbKysnhv8SccWf85utHTS5yYnMJX5hu2idmzZ5OY2JQl1lqTkRhJfmkd723N5/+uCfe70PI2GS7EBGaTiTBzk8ADukX0zRzem39+kcfhs9VU1FqJjwoFmgTyuIwEQsz+sZL0FMFm+xAEIfgQgSwIgl9JjQ2nwdbyEfbUockM7xPLgTNVfHGkmEsHBaZzmcVi4fPPP3eVYTt06FCzEYrwvhcRMWgiCcMuYfOL36VPfJTXfSml6JdgCOR/bz3BR9n5fq/QkRobwcCkKI6crSHUrFxWB28CrztE34QBCYQ4sv9f5pVyzYjeAGQ7uuNNHNCyTvS5QDDZPgRBCD46JJCVUjHALuCPWuuXuzUiQRDOab44Uuz6f3S4GZtdu7KZVwxJ5sCZKjYcKeansy/qsZhyc3NdJdjWrl1LbW2tx/rk5GTmzp3LPvMgiuKHExYdT6ijKkNr4hgMW4Yzk2rTGptV+71CR12DzVVu7f6rh3o07Gh+jO4QfVFhIYzNSGD78TI2HyvhmhG9qW1oZO8po+byuSqQBUEQ2qJDAllrXa2USgKq2x0sCMIFi9aaf35h1ACeP7oPd08d5JnhHJLMaxty2V1QQaXFSlxEaLfEUV9fz+eff+4SxQcPepb1UkoxefJkl5d44sSJNNg0E369EnODjfumD+Lblw9sV2zml9YRHmLCavNeCs0fbMktocFmx6Tg9ksHuiwOPcmUzES2Hy9z1YfeXVDhqkwyvn9Cj8cjCILQ3fhisdgMTAL+0U2xCIJwjrP+cDGHCo376B9eNYSRfT07uk3OTHQ9rt9yrJRZjsf1/iAvL89Vgm3NmjXU1NR4rE9KSmLu3LkuL3FKSorH+k2HiqhtsGFS8J0rMkmKCW/3mBmJka6Jh078XRFh/WEjIz82IyEg4hhgyqAkXl13lL2njBsbZ9Z8SGoMCVFhAYlJEAShO/FFID8KrFFKbQHe0Lr5NBxBEC50XttgZI8vHZTYQhwDRIeHMKF/L7bmlfLFkeIuCeT6+no2bNjg8hLv37/fY71SikmTJrm6102aNAmz2XvbaIAV+woBwzLQEXEMTRURHv5ot0so//pro/xqc3C2sp42NKWdkd3HxAG9MJsUNrtmW14p2Q6BPLG/2CsEQTg/8UUgvwiUYWSQn1dKHQVqm43RWuur/RWcIAjnDocLq/jskCHm7p46qNVxVwxJZmteKRvcvMod5cSJEy7bxOrVq1tkiRMTE5kzZw7z589nzpw5LbLErWG3a1btNwSyr6J9wYR0hveJZf4fNgDQN8F/2eMzFRZXRn7a0GS/7ddXYsJDGN0vnp355Ww+Vsr2c3yCniAIQnv4IpAHYTRvOuF47b9no4IgeMW9vXCwz7h/Ze0RANJ7RXL18NRWx10xJImXVsGRomrOVFjoE9/6eTU0NLBhwwaXdWLv3r0txkyaNMnVvW7y5MltZolbY1dBOWerjPJus0b08Xn7EX3jGZsez66CClbuK+SKIb6LWW+ftfMmIiY8hHEZCT7v059MGZTIzvxyFmefpLzWChgVLgRBEM5HOiyQtdYDuzEOQRCasTi7oEVdW3+WD/Mnb23K4+OdpwAj6/nxzpOtxjo2I4HoMDM1DTa+OFLMjRM9x+Xn57sE8apVq6iu9pwb3KtXL+bMmcO8efOYM2cOvXt3/V7daa8YkhpDZnLnajTPGtHbJZB/9dURKNV2cw93WvusnfaKywYnERrgWsOXZibx18+OUVxt3EjER4YyKDkmoDEJgiB0F1IHWRCCkKIqC48vycFitWPBqCns7/Jh/qKoysKT/93net1ob7vUWajZxKWDklh9oIgvjhZz3ZjefPHFFy4v8Z49e1psM2HCBJeXePLkyYSE+PdX10qHQJ7dBU/0rBF9eGHFIU6W17HvdKVXD7Y33DsPun/WVwxOZoNjgt6VAbRXOJk0sJdHO+sJ/RPa7fAnCIJwriICWRCCkPzSOkzNMpD+Lh/WFs0f97dl9Vi9v8hV8qujsY6Ib+TjXcv55yfZ/P2e3VRXVXmsT0hIYPbs2S4vcZ8+vtseWjuX5uQW13CkyMhSd2XS4LDeMfRPjOJEaS0r9xV2WCAfLarG2qyxSqjJxOeHz1JS0wAEdoKek9iIUEb1i2d3QQXAOdVeWhAEwVdaFchKqTUYnuM5WutGx+v2kEl6guAHEiJDqWuweSyz2vxbPqw1FmcX8PjiHMxmhc2mWTChH4t3nPRq9bBYbfz1s6Mt9tG81JnVamXjxo2uCXY5OTktthk/fryrLvGUKVP8kiXuiE1lyY4CAJKiwxibntDpYymlmDWiN69tyGXlvkLuv2ZYh7b7z85TNLu/oLbBxp6ThhBNi49gQFLrzUp6kl5uZeZe25DL0N6xQWv7EQRB6Apt/QUaBNgB5fZaSrsJQjdjt2t+m7W/xZftzg40rugq7o/7aTSWvbs1H8Cr1eMPqw+TV1KLAkJDTISbm4RoY1Upr31geIlXrlxJZWWlx7FUeDSRA8cT6WjpvOzXN/r1/FqzLrhbPxZnF/DHNcbkwvJaa5ve6Y7gFMh7T1VysryOfu1UtFh3sIj3vjTe3xCTQimw2jQ2rXlz03HjPCrrWbKja3H5g6IqCxuPlrheN9j83zVQEAQhWGhVIDeflCeT9ASh+ymqsvD7VYdZtb8IgF985WLe3XKCo2drsPVA6XHD2tH2GK1hV345ZTUN/MWRPf7hVUO4dXI/lq76nN2b1/LUdx5j165dLbYdO3YsE66YybqafthShqDMxq+gyPAQv9tH8kvrsOvWrR9FlRYe/mg3ziE23XXBN2lAL3pFhVJWa+Wf64/xvRmDve6rqMrCnpMVPPTBbtd2f/zmeE6V1/HO5uMscUx49Fdc/iC/tI6IEDNWW6NrWU/afgRBEHoSv3qQlVLhWut6f+5TEC4UFmcX8Oii3TTYDMU2ZVAid08dRHmtlT+uOcJnh87yxLXdG0NGYqTr+K1R32jn3re3o4CGqlLCTu9i05HXefrmVVRUVHiMjYuLY9asWcyfP5+5c+fSt29fiqosXPn8WizWJt9tfTfYRzISI2lsdi61VhuRISa+OFLMH1YfatEFr6uCL8RsYnBKDNuOl/HGpuP8a+uJFrYOp4XFatfY7Jpws+Klm8eRlhBJmiPj/L+c0x6fQzAI0YzESKx2T6+0v7sGCoIgBAt+EchKqYnA3cDNQJI/9ikIFxJOO4C7KNp1opyiKgvTh6XwxzVHOFRYzanyOr82omhOamwEGb0iySupJcysMJkUC8YbHuQQk6KmroG6UweoO7Ydy7HtNBQaGeRct32MGTPGVZf48ssvJzQ0tMUxnrlhNI8vMewPWsPUIUl+F38JkWEoZWS8nf/a7Jqv/GkDGvCWkO+q4CuqsrCroBwwjmVrVtHDVZ2ksUlo2oHw0KYSbhlJUY4Jmk0BBoMQdf/c3D3dkj0WBOF8pNMCWSmVCNwOfAcYjeFVPuSnuAThgiL3bE3LSgZmI2s4LiOB2IgQqiyNfH7oLLdM7t9tcVisNk6VWwC4f9Ywvj4xHV1bQVrRVv776f/YtH4t1ZWeWWJTWBTTZ87k1hu/xty5c0lPb98ru2BCOlOHJvP8sgN8tP0kB89UY7drv5YNO1RY5Zr89pfbJ/LxjpNk7TnjMSFOAWEhJsLM/hF8+aV1hIeYsNqaJli6Z3/zS+sINZlcnmiAiBCzR3Y4NTaCZxYEpxB1fm7nSvMaQRCEzuKzQFZKzcEQxdcBYRii+Elgkda6ZZsrQRDaZfGOghaVDJxZwxCziWlDk1mac4bPulkg7zlZQb3VSv2pQxzL2sC8n64gOzu7xbjQ5AFEDp5E5KCJxA8cyXuPzfZZLKXGRnDf9CF8tP0kJ8vr2Ha8jMmZif46FVcViJTYcOaM7ENydBirDxTR4Ja9jQkP4aVbxtErKswvgi8jMbKFbcM9+5uRGEm9rX2bQjAL0dTYiKCKRxAEoTvokEBWSg3EEMXfBtKBYuAj4FbgCa314u4KUBDOZ4qqLLy3NZ/3vzRKjYWYFJGh5hZZw+nDUliac4YNh4ux2ux+76pWVFTEsmXL+OObH1KwcR12SzX/z219TEwM11xzDfPmzYP0cby4qcQv2c0hqTGM7hdPzskKPt550q8COcchkEf1jQOc1gXPMVa7nTHp8X4TfE4bwiOLdmN12GUenjPcIzt8xeAk1h48i1IQHmJq9f0TISoIghA42hTISqnbMITxdMAGfAr8GFgKDABu6+4ABeF8ZXF2AY8uznFlNIf1juGt70zhZHnLrOGVw4xGEVX1jezML+eSgV0TkjabjS+//NLVvW7btm0txowYMcLVvW7q1KmEhYW51l0/re3mG77wtXF9yTlZwdKc0yz86kjCQvwj/vecMsrKje5nNOzoKQ/tggnpXDooiTkvfUZVvY1Gt4ltdrvm4BmjKcrXJ6bzszkXiQgWBEEIQtrLIL8NHAPuB/6ttXYVwVRKWowKQmcpqrLwuJs4BjheUovJBBMH9GoxPi0+kot6x3KwsIrPD53tlEA+e/Ysy5cvZ+nSpaxYsYKSkhKP9dHR0ZjTRxMycCJP/eA2fnjdZa3uy5/ZzevG9uWZpfspr7Xy2aGzXepm58Rqs7P/tCGQR/Zr6mjXU9aFvgmRXD8+nbc3H2fJjlPce+VgALbmlXKqwvB4f3+69xJwgiAIQuBpTyDXAwOBrwFlSqnFWuu6bo9KEM5z8kvrWkxICzO3Xcpr+kUpHCys4rNDZ/np7IvaPYbNZmPbtm2u7nXbtm1DNyvdcPHFF7u61/UZNpZrX9kCwNzLRnfyzHwnNS6Cywcns+FIMa9vyGVsRtctD0eKql03H6PdBDL0nHXh+vF9eXvzcfafruRQYRXDesfyn50nARibHs+glJhuj0EQBEHoHO0J5DSaKlW8DbyqlPoIeBM41daGgiC0TkZipEf2GNov5TV9WAp/+/wYuwsqWHOgkFH9WgrJ4uJili9fTlZWFsuWLWuRJY6KiuLqq692lWEbOHCga917W08AkBgdxqDk6C6eoW+kO85747ESpj23lt8uaNkS2hec/uPE6DDS4gOTpZ3QvxcZiZHkl9bx8Y6T/OSaofxv92kAvjauX0BiEgRBEDpGmwJZa10O/An4k1JqAkat428CdwJnMQp1xre2vSAI3kmNjaB/YiTHimsJNSvMJtWuH3bSwF6EmhRWu+YH/zIqSzz9tZH0t58hK8to6bx169YWWeLhw4e7BPG0adOIiPB+jC/zygDD4tGTFqqiKgtLsk+6Xtc32rvcOW6vc4Jev/iA2cGUUlw/rh9/XHOE/+w8xZj0eCotjZgUfGVsWkBiEgRBEDpGh8u8aa2zgWyl1IPAjRhieQbwD6XUTzCqWizpaKk3pdQlGEL7KowJfyXARuDnWusjPpyDIJxz1DY0kl9muJUecNQbbk8MVtRZsWmNra6Skn07qDu2jVtezMZW61mXODIykpkzZ7om2GVmZnYopu3HSwGj7XFPkl9aR5jZRL1bRr2rneOaV7AIFF9zCOST5XX8NusAAFOHpoj3WBAEIcjxuQ6yo5X0u8C7zcq/PQUs9GGfjwBXAB8Cu4E+wI+AHUqpyVrr/b7GJgjnCltzS7HaNCYFt00ZQHxkaKtj7XY72dnZvPbvxZx+fwmWU4dAe9ozhg0b5vISX3nlla1miVvjbFU9eSW1gJGp7km8tTBu6GDr6aKqltU0bHbNvtOeFSwCxZDUGEb1i2PPyUqOO97f68f1DWhMgiAIQvt0qdW01joP+KVS6leAs4FIR3kRuFVr3eBcoJR6H8jBEM93diU2QQhmNh41vMFj0hO8iuPS0lJWrFjh8hIXFRV5rFch4UT0H03s0Ev473M/YvLYEV2Kx5k9DgsxMaqHRaWz/Npji3NcWeTbp/T3mmV1F8Qf7zjF88sOYDYpFPCMw7d89Gw1Fquxn54+F28MSo5mz8lK12tnbIIgCELw0iWB7EQbpsdljp+ObrPRy7LDSqm9wMX+iEu4cGieSfSWWQwmNhwuBmDqkGTAyBLv3LnTVZd48+bN2JtlVYcMGcKwSVeywz6AkPRRmELDeWL+xUweO6jL8Wxz+I/HpscTHmLu8v58xVl+7c5/bmXf6SpqrbYWYxZnF/DY4hy0NjLMTpyd65y+ZWcHvfjIUNJ7tZ+F7k6Kqiws31voseypT/dyzYjUoLwuBUEQBAO/CGR/oYzZNL2BXa2sL29nF4FPFwk9zqLt+Ty2OAeTSWGzayZnJrI1t5Qwswmb1jxzQ9cqIvibkup69p2uxGapxnJwA3f++7csW7aMwkJPIRUREcGMGTNcXuIhQ4YAkF9ay/WvfEFJTQOHCqs6FUPzG4hNx4yM9sVpgfPspsZGcMP4dPad3s/q/UVorV0T7IqqLB4ZZm+EmBT5pXVN/uN+cQGv194d/mpBEASh+wkqgYzRma8f8ESgAxHODYqqLDyyKMfIIjpa+35xxBB7VpuRhexqRQR/obVm586dvPjP9znz8X+pP3mAXzTzEg8ePNjlJZ4+fTpRUVEt9pORGMVPZ1/E40tyWJRdwPdnDG6zpm5zMbw4u8Cjm9x1Y/uy19F17t9bTzAuIyFgNxQzL07lN0v3U1RVz56TlYxON+5580vrsDerztGcOqvhW97rsDMEg73Cm7+6vXJ+giAIQuAJGoGslBoOvAJswKi53AKtdUI7+yhHssgXFMdLarG1I5x6KmPnzdZRXl7OypUrXV7i06dPe2wTHh7OjBkzXKJ46NChHTrWTZPS+fNnR8gvrePZrAN8b/pgr3YSpxg2K0WDzc6k/r3YnFeK1mDBEG4fbCtwjbfadEBvKAanxJCZHE1ucQ2rDxS6BHJcRAhWm+fnHGICs8loS13faMdu15RUN7D3lLOCReB/FfRUe2tBEATBvwSFQFZK9QH+B5QBN2mtZRaL0CGOna2mHX1MfaOt2zN2i7MLeHRxDtpux3o2j6nh+ZzYvZGNGzdis3n6acMT0wgbOIE7brqe5//vVq9Z4vYINZv4ydXDeOjDXazYV8iGI8XYm9lJiqosPL4kx2NS2Kbc0vb3HWALwMzhqby2IZfV+4u4/5phAKzY12Q/iQkPodEhNKcOTSb3bA0PfbSL/NI6fvRuNjUNxvsd6AoWTnqqvbUgCILgPwIukJVS8UAWRub3Cq31mQCHJJxDfLTdyH6aFESHhWC121kwvh+Ls0/SYLNj18ZkrbbKqHWVIwWF/OiZP1N1eBt1x7Zhqy7luNv6sLAwpk+fzvz58xl76QzuXFKAUor7vjW1U+LYyRWDk1AY3XpqG1raSfJL6zA3a2dtAlBgb+OmItAWgKsvNgRyzskKCist9IoK461NeQDcNiWDBRMyPIRmamwET143ku+8sY2jZ2sAiA4z0z+x8++tv+mp9taCIAiCfwioQFZKRQD/BYYBV2utDwYyHuHcYndBuav72x+/OZ4+8ZEu4XT/rGFsOFzMQx/u4mx1A3/97Bj/d3XH7AvtobUmJyfH1b1uwxdfYGts9BgTEt+b6796Ld+++QauuuoqoqON1s3vbjmBUifpFRXKiC5OiDtVYSE81OSRIXbP/mYkRlLfrKRYWKiJR+cO59llB1yP/BeM78fiHSeDxgJwycBEYiNCqLI0suZAEZGhZgor6zEp+P6MIaT3ail8Zw7vzfA+MRw4Uw1AndXGxztPBtXkTEEQBOHcIWACWSllhv/f3r3HR13d+R9/fWZyBULCVZCL3K0IiIBSBBUU0aBWxW7b1Wrtutt27a61+1httZd1+7Pay3Zb7WXbbrutrW3dFZFaTUABC+INEBGUiyi3IJdwSwgkk8vM+f3xnQwzk0lIyExmkryfj8c8Er5z5nzPdw6T+cyZzzmH/wVmADc4515PV1ukc/r16p0AfGRQAfMnDo5ZsWBgQR4Lpgxl24EqfrFqBz9esZ3RA3py0ci+ZxT8HT9+nGXLlkWC4g8//DC2gC+LvGETyB81lfxR08jqN5SbFkzkuunnxBRbvsVLFZhyTh98vvatsDCsb743fBwlevS3T48c8rN9VNUGyfEbvvB21gumDGX+pMExX/nfc9W4jEkByPb7uHzcAJ7buJ/lWw5y8HgtAMUTBicMjsFLJ9lxuDry75DLnMmZIiLS+aRzBPkHwMfwRpD7mtmno+474ZxbnJZWSadwoDLA8xu9CW93zhrZ7HJeX5o7lv9dW0ZFTT13P7mBbJ9FNpRoiXOOd999l9LSUkpKSli9ejUNcaPEQ4cNY8L02Wy0EfiHTiIrN5+eOVmcrGsg5ODbz29hzMAC/D5jWN98Vm07xPKt3oYfK7cdYtH6ve0a4RxYkMfDCybylac3Riaw3X3F2EhAuPTdA1TVBjHgx7dM4cLhRTFpCdGBY6alAMw976xwgFwe+Qzwd7Oa3zK77GgNuX4fdVpOTUREkiCdAfLk8M/rw7dou4HFHdkYSZ+2bupRXhXgu0u20hBy9O+Vy8da2Lr3RG0DJ+u8wDYYcgRDza/SUFVVxfLlyyOjxGVlZTH3Z2dnc+mll1JcXEz2iCn87K0A20KObAdF+dk8fdclVFTX0yvXzx2/Wcv+ygCf/MVr5Gb5IvnQjRpaaEdbLJgylEtG9+emn73C/soA26LWRf7dq14m9Lzzz+Lq8wed8TnS4fJxA4BTA+RmsPvISaaek3gbbC2nJiIiyZS2ANk5Nztd55bMEb8m7+k29WjcTa1x44Wp5xS1uPNb2dEa8rL81AdPjf42jiwO6JXL5s2bIwHxyy+/TH19fczjhw0bRnFxMcXFxVx55ZUUFBRQXhXgsu+9RG3UsmPVdUEK8rIYHV6P+KGbJnDnb9fhgEAzm1ska4RzUGEe/3LVOO5duJG/vL2Pe+aOo6YuyJpd3ooVn5kxol31p0N9KIQZkRVK3GlSJrScmoiIJFPaV7GQ7it6d7TGNXlbCoLKqwI8ELeb2l+3HaK8KtBsIBQ/shiqq+Hojk089q2F/HXZC+zZsyemfFZWVmSUuLi4mPPPP79J+kbZkWriBivJzYoNdovyc8jN8rW481syRzhvvHAIjy7fzt5jNfz0pffJ9nttHjOwFzNG90vKOTrSmexAp+XUREQkWRQgS9qUHa1pcixRENSYgvHegSrq49Yny/G3HDQN6JXLXZPzeOjnf6Rq+1oCe9+FYAO/iyozZMiQyHbOV155Jb17N7+6hHOOxRu8JeSixQe7w/rmE58W3bixRY4/+SOc2X4fd80ewwPPbGLR+r2R5d0+M+OctG+3fCaG9c0nvtWt+UCRabnUIiLSOSlA7sbic3/bmgvcXsP65jcNNIOxQdCi9Xt5YNEmHCQcjU0UNJ08eZIVK1ZEJtjt3r079kE+P5fOmsl1115LcXExEyZMaFUQWX48wIN/eZeSTd5S3dFrL8cHu8195Z/KEc6bpw7hu0u2UFnTQCic/tG401xn0zgBUSkTIiKSDgqQu6nGwNPvMxpCjqvGn8WLmw+S5TeCIXfaXOBkCIVosgveVePPigRB0SkY0bL95uUVh4OmAb1y2bp1aySXeOXKldTV1cU85uyzz2b23HmsOHE22cMv4O5PX8LHp7b++v7wxm6+ufjdyLbWF43ow4//dgofVjQf7Db3lX+qgrzKmnpO1sbu2vet595l7viBnTKwVMqEiIikiwLkbihR4PlceMm02vBcto5YQ7b0He+cvfOzmD6yHy9uPsiyLeXsPVbN0D492Hagivq4EeZeuX5+9KkLybcGdm1aw/JfP8K/lpayc+fOmHJ+v5+ZM2dGcoknTZqEmXH3n97i2bf38firu7h5ypAWR47LqwLsPHSSl7aW8/NVO2Lu27S3Ep+PZldVaNSRX/mXHa0hL9vPidqmExI7a3CplAkREUkHBcjdUKLc33h+s5QHViWbvAD5mvMH8fXrxjP3Byspr6rl/kWb+PtZI3no+c2RpdGcczQc28eh3W/y7dU/4LVXXqa2tjamvsGDB0cC4rlz51JUVNTknJ+55ByefXsfmz6sZENZBRcOTxzgLlq/l688vZGGkGsyyg1ezm+mBZ7D+ubToKXORERE2k0Bcjc0rE9+zIYKiVTXBenXMydlbTh4PMC63d420fMnDqZ3XjbfvH48//THt3h5+2FWbz9MsD5A7Z5N1O58k+odb1J/zAuoy8N1+P1+ZsyYQXFxMfPnz+eCCy44bS7xlOF9GD+4N5v3H+eHy97jP/7mgiZBbnlVIGbzjUQyMfDUUmciIiLJoQC5G3rv4InIBgw9cvyEnGPBhUNY9NaH+MyorgvigP94YRvfvG48ZceSnwNaumk/zkFhfjYzx/QH4OIRfQlW7OPk++uo2fEmtWWbcA2xucSDBg3immuuobi4mKuuuoo+fVpOcYhnZkwY4gXIq947zKXffYlH4nbWKztSTTButYxcv+EwcrMyO/BU3q6IiEj7KUDuhn692sunnTy0iG9cPz4SSN1z1TjKjtbwxo7DfG/pezy3cT9L3jlAfra/VZt4tEXJO95KEHPGFLL8xRcoKSlh8V+eZ++u2FxfzMcFUy/ib268nuLiYiZPnoyvHSszlFcF+POGfZF/1zaEmuRblx2tIS4+xnzGM3ddQk1dKOMDT+XtioiItI8C5G7m/fITvLTtEABfmD06ZpJZY2A1ZXgRG/dWsuTdgzSEHFXhSV/Jmri3ZsNmlj/9ONUfrOPnj27m0UBsTrSvRxH5o6aQP2oaRWOn8sK/fSxpAV+iDSji863/uNbbPCR+GbfxgwuT0gYRERHJbAqQu5nfvOKt9jC8bw+uGn9WwjJmxu0zRvDC5oMxI6lnuiJCIBBg5cqVkXWJt2/f3uR8H/3oRykuLiZv1FR+tcWR40+8vnB7xe+sB1BTH4zkE6/fc4w1O70tmh/71IUMLsrP+BFjERERSS4FyN3IsZN1PL1+LwCfnTkisttaImPO6oXfZ5ENJ6BtE9N27txJSUkJpaWlrFixgpqa+FHiQsZMmcWDd93KvHnz6Nfv1HbIn0nhhiXRE9kAAvUhQg427zvOwHPz+MXKDwA4/+zeXDtpcKfchU5ERETaRwFyGyXaba6jd6A7U798eQeB+hA9c/z8zbRhLZZtDCTvXbgRgCyftTiaW1tby6pVqyJB8bZt22LuNzMuvvhiBk+4hNfrh5EzaAyhLD+5506KCY4bz53K5zEyke1INQ+XbOXNPcf4f89tZnBhPi9sPgjAFy4freBYRESkmzKXaJHXTsrMKgoLCwsrKipSUn/87nP/ctU4AP7zxffI9vtoSPJEtmT6v7Vl3Pe0F+z6fcb3Pz6pVe386tMbeXJtGcP75rPy3jkxQeOuXbsiaRMrVqyguro65rH9+/fn8ivmcuHMK1jwsWIasnpxw09fiUnbyMv2seq+OWn7YLFl/3GufexlQg4G9Mrl0IlahhTls/Le2WT5O+c2zSIiInJ6RUVFVFZWVjrniuLvU4DcSuVVAS773ksE6ltePzjdAV8i5VUBZn5nRcy6vq1t57v7Krn2sdUAPHnnVE7sfieypfOWLVtiypoZF110UWSzjj2+QXzj2c1k+Yya+hAu5Ih/9gpys/jt31182h3pUukbi9/h96/vjvw7y2d8r5UfIERERKRzailAVopFK5UdrcE4/VfuHbW1b1vSOhItW9badvaqr6DnByvYueEVLnt0I/VxK0707ds3si7x1VdfzYABAyLtu/27L8WsFpFIJmy4cfuMc2IC5IaQ65CttkVERCQzKUBupYK8LGrqgzHHcvwGGHXBU0FgoCGY8oBv0fq9TXZLa2m0M9tvTTa+aC4wraurY/Xq1ZFc4s2bNzcpM23aNObPn09xcTEXXXQRfr8/cl95VYBdh09Suml/k936crJ83DlzBL95dVdG7fR2PNBAXrYv5tuBjvqgIyIiIplHAXIrOOf4/tJTk8565vgJOsfDN00E4IFFm6gPOoLO4TPYe7Q6ZZP2yqsCPPDMJgL1IQLhhIXTjXa+tPVQ5PeC3KbLp5WVlUXSJpYtW8aJEydiHl9U1If6wRPJGzWNx/71Dm6ZPTHheRat38tXn95EQyjUZMQavHWFPztrJJ+dNTKjJjUm+qCQCSPbIiIikh4KkE+jvCrA/7y8kxfDqxv82/XjmTS0KCa4mzW2PxvLKrlv4UaOVtdx83+9Rs/crJRM2is7WtPkWH3QsXLbIUYN6NUk6AyFHE+9WQbAbTOGc+PkoQwqyGL7xje5777HKC0t5Z133mlS59SpUykuLmb+/PlcfPHF/MPv17NiaznLdwW4JUG7yqsC3L9oU8xougHZfl/C7ZkzITBuFL30WyaNbIuIiEh6dLsAuS25u4vW7+WrizZFUgUmDyvijktGNFn+a2BBHnPH5/HIggl8/on1OOBEknefa9SvZ06TiYLBkOPehRvJzfJhwMMLTgXlr35whL3Hamg4fpj8HTv59uMrWLZsGVVVVTF1FBUVcfXVV0dyiQcNGhRz/81ThrJiazkvbStn+ZaDTBxaGHNNZUdriF8VrVduFj/81GT69MjJmNHi5kSWfsugkW0RERFJj24VILcld7e8KsADUcExeEuCHTpR22zw1L8gj9ys2G2Mk53L+kTUZLKeOX5qG4I0nq7xvA88s4npIwp5f9N6/uUHv2Xf63+l/tAuvhZX14UXXhjJJZ4+fTpZWc3/d7jyvIHkZRmBBsddf1iPGTHP37C++U1yjutDISbFBdKZLNXrL4uIiEjn0G0C5PKqAF992ksBaMzdvX/RJj4yuICaulCTUcNEKz/k+FsOdof1zW+yzkUyc1nf3VfJb17dBcA/Xj6KueMHcay6jnuefIsTtUEaqo5Qs+NNjux8k3Mf3cjJuFHiwsJC5s2bx/z587n66qsZPHhwq899PFBP4xzF6EC8cXTcZ0bjioF52d76wUpTEBERkc6o2wTIH5SfoD4UO8JZ2xDiusdWk5vlx+FiRkTrg6GYfFo4fbA7sCCPhxdMjATiAJ+cNiwpQeKBygD3PLmBYMgxZmAvvnzVuZgLUrJ8JfuX/Q9V76+jvnxnk8dlDxxF77EX8advfYE5l81qcZS4JWVHa8jN9lFdd2olj+jR8efe3ocDeub6+dXt0xg9sJeCYxEREemUuk2A/NzGfSTaEyXkiCzf1jgi2rdHDo+UeJtgGMRMuDtd0NeYy3rXE+tZt/sYK987RH0wRHY7dmVbtH4v9y3cSEPI0XDiKH3q3ubWv/0JL774IpWVlTFlLbcH+SMuJH/UVPJGTiWroB9+M6qKxpxxcAze6HjINb9U3OIN+wC4buLZzBjd/4zPIyIiIpJu3SJAfnP3Mf60xlvJIctn5Gf7CTR4QXH07nLBkGNDWQUvv3eYt/d6ged/fXoKAwry2jRxa2BBHt+5eSLzfriKXUeq+dOaPdw+Y8QZtX3fsRPc8+iTVG5fS80H66gv38HCuDKTJk3i8rnzmDh9NldePouvPPMua3cdO3Vdrv0bXzSu9BA9Ov7p6cMZWJDHrsMn2VBWAcANF559RvWLiIiIZIouHyAH6oPct/BtQg7OG9ybX90+jQPHA+Tn+Fjws1djAuT6oOPzv38zMtI8c0w/rpnQ+jzdaGMGFvDJi4bzpzV7+OEL7zGyf0/OHVTQqgD1wIEDLFmyhNLSUkqWLOXE8dhRYl9ODy6/4gpuufkGrrnmGoYOjZ1o+KW547j912/E5FAnY7Jg4+j4Pzy+jrf3VrJudwXOORZv+BCAQb3z+OjIfmdcv4iIiEgm6NIBcnlVgIef38oHh06S5TO+//FJDOmTz5A+XlpA9Nq3J+saCDli0jDW7TpGeVXgjIPKL88dy8J1ZRyrqefO367D5yPhyhnBYJAlL61i0Z+fZ93qFWzc8FaTurL7n0P+6Gnkj5pK4YjzefL+ec22a9xZvcj2x66mkazJggML8vjateP5xC9eY0NZBa+8f4Q/h9Mrbph8Nj7f6bfjFhEREclkXTZAbtzVrTEdYM65A5gwpDCmTPTat8eq6/jiH9bHBJWnW7XitAwa4+26YAiCp/KcXXUlS5cupaSkhOdKl3AybpS4Z69e9B83jaoBE8gfNZX8PgPJy/K3ahOLgQV5PLIgdRtfXDyyL9NH9uWNnUf516fe5sDxAAA3TB6SlPpFRERE0qnLBcjBkGPT3orIpLZGL28/nHA0uHHt2/KqQJONLto76lp2tIa8bB8naoO4UJC6/e9Rvfst5jz7NTZvTDRKPJz8UdPIHzWNHsPGE/JlUQDMPW8gDy+Y2KZNLFK98cXdV47l1l+9EQmODdiyv5LxZ/dO6nlEREREOlqXC5Cr64Jc/5NXmhzPPs1ocCq2G84LVnFkwzKOb19HYOd6QgFvXeJD4ft79uzJyEkfZV/vj5A/aipZvQdGHhu9wNzq7YcBmHpOnzadP5UbX4wd2AuzUykpDvja4ne4dNwALe8mIiIinVqXC5ATrOQGtG40uL2jrsFgkHXr1lFSUkJpaSnr1q3DxS2NljdgOJ+/9Wauv+5axk2axvyfvk5BoKHFek8X3KdD2bEa8rJ81NSnbtdAERERkXTocgFyo2y/YRi5WW0bDW7rqOvhw4dZunQppaWlLFmyhCNHjsTc36NHD2ZdPodRU2ZRemwQFAxgyJzRXHHFuXzu929SFWggP9tHyHk5z3XBICEXu/xcMnfjS5ZhffObfBjJxHaKiIiItFVaA2QzGwx8CZgOTAN6AXOcc39tb91+n7HorksSbiPdHqFQiHXr1lFaWkppaSlr1qxpMkr8kY98hOLiYubPn8+ll15Kbm4uAP/5wjYeW/E+v1j5AeVVtby4+SAA37l5EjNG94uMXK/efjhlE+ySJRUpKSIiIiKZwOKDuw49udls4CXgfaAcuIR2BMhmVuHL7Vk49t6nEi6ndqaOHDkSGSVeunQphw4dirm/R48eXHHFFRQXF1NcXMzIkSMT1hOoDzLzO8s5crI+cuz8s3vz3D/PwuJmCJZXBVI2wS6ZOks7RURERKIVFRVRWVlZ6Zwrir8v3SkWbwL9nXNHzOxG4Jn2Vtgjx8+q++a0K1gLhUKsX78+kku8Zs0aQqFQTJlx48Yxf/58iouLueyyy8jLO/35jgfqOR6Xb/x++QkOnahtdnWNTNdZ2ikiIiLSWmkNkJ1zVcmu0++zMwrYjh49ygsvvBDJJS4vL4+5Pz8/nzlz5kSC4lGjRrX5HGVHa7y1jIOnguR2r7UsIiIiIkmV7hHkNjGzitMUKTzN/RGhUIi33norkkv8+uuvNxklHjt2bCSX+LLLLiM/v30T0Ib1zac+7hya2CYiIiKSWTpVgNxex44dixklPnjwYMz9eXl5zJkzJ5JLPGbMmKSeXxPbRERERDJfpwqQEyVRRwuPMEdGkZ1zbNiwgdLSUkpKSnjttdeajBKPHj06kjYxe/bsdo8Sn06qd7gTERERkfbpVAFyazjneOqppyKpEwcOHIi5Pzc3l9mzZ0eC4rFjx3Z4GzWxTURERCRzdbkA+fjx43ziE5+IOTZq1KhILvHs2bPp0aNHmlonIiIiIpmuywXIADk5OcyePTuSSzxu3Lgm6wyLiIiIiCTS5QLknj17cvDgQXr27JnupoiIiIhIJ5T2ANnMvh7+9bzwz9vMbBZQ4Zz7SVvry8rKUnAsIiIiImcsrVtNA5hZcw3Y7Zwb0ca6KgoLCwsrKira3S4RERER6boyeatpnHNKDhYRERGRjOFLdwNERERERDKJAmQRERERkSgKkEVEREREoihAFhERERGJogBZRERERCSKAmQRERERkShpXwc5mcwsBFhhYWG6myIiIiIiGayyshLAOeeaDBh3tQC58WIq09qQ5OgV/nkira1ov8ZPK+qTzNJV+kV9knnUJ5lHfZKZukq/dOY+6Q2EnHNN9gVJ+0YhSdb4UaAoze1oNzP7K4BzbnZ6W9I+ZlYB6pNM01X6RX2SedQnmUd9kpm6Sr90pT6JphxkEREREZEoCpBFRERERKIoQBYRERERiaIAWUREREQkSldbxaICul6ieGemPslM6pfMoz7JPOqTzKM+yTxdtU80giwiIiIiEkUBsoiIiIhIlC6VYiEiIiIi0l4aQRYRERERiaIAWUREREQkigJkEREREZEoCpBFRERERKIoQBYRERERiZIRAbKZDTaz75jZS2ZWZWbOzGYnKFdoZj81s/1mFjCzt83sllbUXxKu80cJ7htiZk+Y2WEzqzaz181sXlIurBNLdp+Y2YPhOuJvBxKU/ZqZ/dnMDoTLPJiSi+xk0tUnZtbXzB43sy3h81aa2Vozu83MLHVX3Dmk+bWSqJwzsy+k5mo7hzS+Vu5ooU+cmd2auqvObGl+neh9PoFk90lU+VvMbI2ZnTSzo2a20swujrq/l5n9u5ktCd/vzOyOlFxkO2SluwFh5wJfAd4HNgKXxBcwsyzgReAC4CfhslcDfzCzLOfc7xJVbGbXApc1c18RsBroCzwKHAQ+AZSY2Tzn3Ir2XVanlqo++TxQHfXvmgRlHsLri7eAa9pxDV1NuvqkNzAKeAbYA/iBucDvgLHAN8/8krqEdL5WAJYCT8Qde6MtF9AFpatPVgG3JXjcl8PnWd62y+hS0tInep9vUdL7xMweCtf5e+CXQM/wYwdFFeuP975RBmwA5iTzopLGOZf2G1AA9Av/fiPggNlxZT4ZPn573PGFeP/hcxLUmwO8h9cRDvhR3P1fCR+/LOqYD1gDbEj389KV+gR4MFy2qBXnHhH+WRR+zIPpfj4y4ZbOPmmmPc8Cxwmvp95db2l+rTT5u6ZbZr1WgPzw6+SFdD8v3bFP0Pt8R/bJJUAIuOk0580FBod/nxyu/450Px/xt4xIsXDOVTnnjpym2Ey8J/H/4o4/CQwk8SeQL+H9cfqPFurc75xbFdWWUPgcF5jZua1ofpeUwj4xM+tt1vxX8865XW1pa3eRzj5pxm680YHsNj6uS8mEfjGzfDPLa1WDu4FM6JMo1+MFIn9ow2O6nDT2id7nm5GCPvkSsNY594yZ+cysVzPnrXXO7T/TdneUjAiQWykXaADq4o43frUyJfqgmQ0CvgE84JyrJrFcEn9tmbBOaaJNfRK2B6gEKs3sf8ysbwrb1x2lrE/MLM/M+pvZCDO7HfgssNo5F38uaSqVr5W/B04CNWa20cxuSkaDu4GO+vt1K977zKIzbWg3koo+0ft8+7SlT64E1prZw3h9UmVmuzpr7n2m5CC3xja8kaqLgdejjl8a/nl2XPlHwo+Jz82Lr/NKMxvqnNvbijolVlv65Bjw43C5OuAKvNyxKWY23TlXm/rmdgup7JO/D5dvtBy4I2kt79pS1S+vAv8L7ASG4Y3gLDKzW5xzf0rFhXQhKf/7FQ7WrgEWO+eqktv8LikVfaL3+fZpVZ+YWR+gH/ApIIiX2nIU+CLwhJlVO+ee6ahGJ0W6czwS5KbcSOI8mEFABbAVb4LQCOBzeJ9SHPCrqLIX43XQrKhjiXKQJ+G9sF4DZuBNRLofCITLfz3dz0cm3JLRJ83Ue1e43D80c38RykHOmD4Bhobr/BTeBIxlwLh0PxeZdEvXayWqXE+8YLmMbp4bngl9Eq7PAR9L9/OQSbeO7BP0Pt8hfYL3Ad2Fb9OjHp8b/nv0VjPnnYxykNvHOXcA+BheTvGLeG8C3wf+OVzkBHjJSHgzVZ92zq0+TZ0bgVvwZuK/CnwA3A3cE12nJNbaPmnBz/G+prkyVW3sblLZJ865vc65Zc65J51zt+FNgF1mZvnJan9X1VGvFefcyXDZoXgz1KUZHdQnt+KNopW2q7HdRCr6RO/z7dOGPmlMY9npnHsj6vG1eBP6LmguJzlTdaYUC5xzq8xsFDARb6TkbU59PbI9/PMmvBHkB8xsRFwVvcPHDjrnasJ1LjSzZ/GWIfED64HZcXVKM1rZJ809NmRmH+ItvyNJ0oF9shD4R7xlFJeeeYu7hw7sl7LwT72uTiOVfWJmw/G+hv6lc64+ea3u2lLRJ3qfb59W9slRoBZvZYt4BwEDCulEH0g6VYAM4JwL4q2bB4CZzQ3/2riW4XC8yYeJ1jb8bPhWDCyJqrMOWBtXZy3wShKb3mW1ok8SMrNsvK9l1rZUTtqug/qkceS48Aya2C11UL+MCv88dAZN7HZS2Cd/ixcUdOvVK85EKvpE7/Ptc7o+CX842QAMSfDwoXhpr0dT28rk6jQpFomY2QC8RPClzrkt4cN/wRtFjr8BPBf+fX0LdY4FvgD81jlXkZqWd13N9Enj8Xj3Anlo9DGl2tsnzZQDuBMvd6zZ15M0Lwn90j9Bnf3w8jB3Ouc0MtZGSf77dQveCgstpvpJy1LxnqL3+fZprk+Ap4BhZnZVVNneeBuzvNr4zX1nkTEjyGb29fCv54V/3mZms4AK59xPwmVW4/2xeR8vcfzzeEH+5xvrcc59gJdjFF8/wAfOucVRx7LwvipYiPeHbBTei2YP8NXkXV3nlKw+CdttZk8C7+B9ap8D3Bx+7B/jznsbcA7eHzqAy6La8mPnXGVyrrDzSVOffNHMbgSeB3YBfYAFwHTgZ86595N4iZ1Smvrln8zsBrwP/nvwRm4+h7c26Y3JvL7OKF1/v8L1TsCbHPYdF56JJOnpE73PtyzJffJfeKsdPW1mP8RbaeROvAn398ed95/Cxxt32LvezIYCOOceStLltU+6Zwk23jg1+zH+tiuqzKPADrwXwwHg18DZbaj/R3HHfHhLJJWF69wD/CdQmO7nIxNuyewT4L+BzUBVuOw24FtAfoKyf23h3CPS/bx0tz7By6N8Jup1UoU32eXv0EoJ6eyXeXiTZg7gzdI/ivchZma6n49MuKXr71e4/CPhc01M9/OQSbc0vU70Pt9BfRIuOwhvlaOjeBP3VhO1i2FUuV3NnTvdz0njzcINFREREREROnkOsoiIiIhIsilAFhERERGJogBZRERERCSKAmQRERERkSgKkEVEREREoihAFhERERGJogBZRERERCSKAmQRkU7MzH5rZlrQXkQkiTJmq2kREYE2BrsjU9YQEZFuTDvpiYhkEDP7dNyhS4HPAb8EXo677xm8bab9zrlABzRPRKRb0AiyiEgGcc49Ef1vM8vCC5Bfi78vSn3KGyYi0o0oB1lEpBNLlIPceMzM+oV/P2xmVWa22MwGhct8zsy2mFnAzLaa2Q3N1P9JM1sdfny1mb1hZh/viGsTEUkXBcgiIl3XEqAQ+Cbw38B1wDNmdi9wL/A48FUgB1hoZjE5zWb2EPAkUAV8I1y2GnjKzL7YURchItLRlGIhItJ1rXHORQJZMwP4MjAEmOCcOx4+vgJ4Gy+V4/7wsSnA14BHnHMPRNX5mJktBh4xs98556o64kJERDqSRpBFRLquH8X9u3GS3+8ag2MA59xG4DgwNqrsrYADHjez/tE34FmgAJiRspaLiKSRRpBFRLquHXH/Phb+uTNB2WNAv6h/nwcYsLWF+s8686aJiGQuBcgiIl2Ucy7YzF3NHbe43x1Q3EL5d8+waSIiGU0BsoiIJLIduAbY45zbku7GiIh0JOUgi4hIIr8P/3zYzPzxd5qZ0itEpMvSCLKIiDThnFtrZg8CDwIbzOwpYB8wGJgKzMdbHk5EpMtRgCwiIgk55/7dzNYBdwP3AD2BcuCd8DERkS7JnHOnLyUiIiIi0k0oB1lEREREJIoCZBERERGRKAqQRURERESiKEAWEREREYmiAFlEREREJIoCZBERERGRKAqQRURERESiKEAWEREREYmiAFlEREREJMr/Bwpm4+jGKHSKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "\n",
    "df_train[target].plot(ax=ax, marker=\".\", label=\"train\")\n",
    "y_pred_train.plot(color=\"k\", ax=ax)\n",
    "y_pred_test.plot(color=\"k\", ax=ax, linestyle=\"--\")\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"y_pred train set\", \"y_pred test set\"])\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Air passengers\")\n",
    "ax.set_title(\"Air passengers - Linear Regression\")\n",
    "ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n",
    "plt.tight_layout()"
   ]
  },
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    "We can see how specifying the start time has no effect on the model, we get the same linear curve! Hopefully this further reinforces the point that this feature cannot create look-ahead bias."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04804241-3716-4261-9cba-50f96e520383",
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   "source": [
    "We can see how the time feature enables linear models to extrapolate a linear trend. We also see the limitation of tree-based models when the data contains trend. We will discuss how to handle trends when working with tree-based models in a later lecture."
   ]
  }
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